Predictive control using one or more neural networks

ABSTRACT

Systems and methods for cooling a computer environment are disclosed. In at least one embodiment, one or more neural networks can be used to determine one or more temperature control settings associated with one or more servers.

FIELD

At least one embodiment pertains to processing resources used to performand facilitate artificial intelligence. For example, at least oneembodiment pertains to processors or computing systems used to trainneural networks according to various novel techniques described herein.

BACKGROUND

In computing environments such as data centers, control infrastructureis used to manage aspects such as power distribution and temperaturecontrol. Various sensors or monitoring components may be used todetermine a current state of a computing environment, and this stateinformation can be used to determine adjustments to be made to thecontrol infrastructure. This can include adjusting power or temperaturecontrol devices based upon detected power or thermal conditions. Thepurely reactive nature of these approaches can allow for undesirableoccurrences, such as excessive power draw or thermal events, which mayresult in damage to hardware or loss of data. While modeling approachesexist to help better determine a state of such an environment, thecomputational complexity of these approaches prevents them from beingused in real time to make dynamic adjustments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A, 1B, and 1C illustrate a computing environments, according toat least one embodiment;

FIGS. 2A and 2B illustrate temperature distributions in a datacenter,according to at least one embodiment;

FIGS. 3A and 3B illustrate server racks with neural network-enabledcooling systems, according to at least one embodiment;

FIGS. 4A and 4B illustrate data centers with neural network-enabledcooling systems, according to at least one embodiment;

FIG. 5A illustrates a process for adjusting environmental controls in acomputing environment, according to at least one embodiment;

FIG. 5B illustrates a process for determining temperature controlsettings, according to at least one embodiment;

FIG. 6 illustrates a distributed system, in accordance with at least oneembodiment;

FIG. 7 illustrates an exemplary datacenter, in accordance with at leastone embodiment;

FIG. 8 illustrates a client-server network, in accordance with at leastone embodiment;

FIG. 9 illustrates a computer network, in accordance with at least oneembodiment;

FIG. 10A illustrates a networked computer system, in accordance with atleast one embodiment;

FIG. 10B illustrates a networked computer system, in accordance with atleast one embodiment;

FIG. 10C illustrates a networked computer system, in accordance with atleast one embodiment;

FIG. 11 illustrates one or more components of a system environment inwhich services may be offered as third party network services, inaccordance with at least one embodiment;

FIG. 12 illustrates a cloud computing environment, in accordance with atleast one embodiment;

FIG. 13 illustrates a set of functional abstraction layers provided by acloud computing environment, in accordance with at least one embodiment;

FIG. 14 illustrates a supercomputer at a chip level, in accordance withat least one embodiment;

FIG. 15 illustrates a supercomputer at a rack module level, inaccordance with at least one embodiment;

FIG. 16 illustrates a supercomputer at a rack level, in accordance withat least one embodiment;

FIG. 17 illustrates a supercomputer at a whole system level, inaccordance with at least one embodiment;

FIG. 18A illustrates inference and/or training logic, in accordance withat least one embodiment;

FIG. 18B illustrates inference and/or training logic, in accordance withat least one embodiment;

FIG. 19 illustrates training and deployment of a neural network, inaccordance with at least one embodiment;

FIG. 20 illustrates an architecture of a system of a network, inaccordance with at least one embodiment;

FIG. 21 illustrates an architecture of a system of a network, inaccordance with at least one embodiment;

FIG. 22 illustrates a control plane protocol stack, in accordance withat least one embodiment;

FIG. 23 illustrates a user plane protocol stack, in accordance with atleast one embodiment;

FIG. 24 illustrates components of a core network, in accordance with atleast one embodiment;

FIG. 25 illustrates components of a system to support network functionvirtualization (NFV), in accordance with at least one embodiment;

FIG. 26 illustrates a processing system, in accordance with at least oneembodiment;

FIG. 27 illustrates a computer system, in accordance with at least oneembodiment;

FIG. 28 illustrates a system, in accordance with at least oneembodiment;

FIG. 29 illustrates an exemplary integrated circuit, in accordance withat least one embodiment;

FIG. 30 illustrates a computing system, according to at least oneembodiment;

FIG. 31 illustrates an APU, in accordance with at least one embodiment;

FIG. 32 illustrates a CPU, in accordance with at least one embodiment;

FIG. 33 illustrates an exemplary accelerator integration slice, inaccordance with at least one embodiment;

FIGS. 34A-34B illustrate exemplary graphics processors, in accordancewith at least one embodiment;

FIG. 35A illustrates a graphics core, in accordance with at least oneembodiment;

FIG. 35B illustrates a GPGPU, in accordance with at least oneembodiment;

FIG. 36A illustrates a parallel processor, in accordance with at leastone embodiment;

FIG. 36B illustrates a processing cluster, in accordance with at leastone embodiment;

FIG. 36C illustrates a graphics multiprocessor, in accordance with atleast one embodiment;

FIG. 37 illustrates a software stack of a programming platform, inaccordance with at least one embodiment;

FIG. 38 illustrates a CUDA implementation of a software stack of FIG.37, in accordance with at least one embodiment;

FIG. 39 illustrates a ROCm implementation of a software stack of FIG.37, in accordance with at least one embodiment;

FIG. 40 illustrates an OpenCL implementation of a software stack of FIG.37, in accordance with at least one embodiment;

FIG. 41 illustrates software that is supported by a programmingplatform, in accordance with at least one embodiment; and

FIG. 42 illustrates compiling code to execute on programming platformsof FIGS. 37-40, in accordance with at least one embodiment.

DETAILED DESCRIPTION

In at least one embodiment, a computing environment may include avariety of computing devices and control systems, as illustrated in datacenter 100 illustrated in FIG. 1A. In at least one embodiment,datacenter 100 may include one or more rooms 102 having racks 110 andauxiliary equipment used to house one or more servers on one or moreserver trays. In at least one embodiment, datacenter 100 is supported bya cooling tower 104 located external to datacenter 100. In at least oneembodiment, cooling tower 104 dissipates heat from within datacenter 100by acting on a primary cooling loop 106. In at least one embodiment, acooling distribution unit (CDU) 112 is used between primary cooling loop106 and a second or secondary cooling loop 108 to enable extraction ofheat from second or secondary cooling loop 108 to primary cooling loop106. In at least one embodiment, secondary cooling loop 108 can accessvarious plumbing into a server tray as required. In at least oneembodiment, loops 106, 108 are illustrated as line drawings, but aperson of ordinary skill would recognize that one or more plumbingfeatures may be used. In at least one embodiment, flexible polyvinylchloride (PVC) pipes may be used along with associated plumbing to movefluid along in each of loops 106, 108. In at least one embodiment, oneor more coolant pumps may be used to maintain pressure differenceswithin loops 106, 108 to enable movement of coolant according totemperature sensors in various locations, including in this room, in oneor more racks 110, and/or in server boxes or server trays within theseracks 110.

In at least one embodiment, coolant in primary cooling loop 106 and insecondary cooling loop 108 may be at least water and an additive, forinstance, glycol or propylene glycol. In operation, in at least oneembodiment, each of primary and secondary cooling loops has their owncoolant. In at least one embodiment, coolant in secondary cooling loopsmay be proprietary to requirements of components in server trays orracks 110. In at least one embodiment, CDU 112 is capable ofsophisticated control of coolants, independently or concurrently, inloops 106, 108. In at least one embodiment, CDU may be adapted tocontrol flow rate so that coolant(s) is appropriately distributed toextract heat generated within racks 110. In at least one embodiment,more flexible tubing 114 is provided from secondary cooling loop 108 toenter each server tray and to provide coolant to electrical and/orcomputing components.

In at least one embodiment, electrical and/or computing components areused interchangeably to refer to heat-generating components that benefitfrom a datacenter cooling system. In at least one embodiment, tubing 118that forms part of secondary cooling loop 108 may be referred to as roommanifolds. Separately, in at least one embodiment, tubing 116 extendingfrom tubing 118 may also be part of secondary cooling loop 108 but maybe referred to as row manifolds. In at least one embodiment, tubing 114enters racks as part of secondary cooling loop 108 but may be referredto as rack cooling manifold. In at least one embodiment, row manifolds116 extend to all racks along a row in datacenter 100. In at least oneembodiment, plumbing of secondary cooling loop 108, including manifolds118, 116, and 114 may be improved. In at least one embodiment, a chiller120 may be provided in a primary cooling loop within datacenter 102 tosupport cooling before a cooling tower. In at least one embodiment, toan extent additional loops exist in a primary control loop, additionalloops may provide cooling external to this rack and external to thissecondary cooling loop; and may be taken together with this primarycooling loop.

In at least one embodiment, in operation, heat generated within servertrays of racks 110 may be transferred to a coolant exiting racks 110 viaflexible tubing of row manifold 114 of second cooling loop 108. In atleast one embodiment, second coolant (in secondary cooling loop 108)from CDU 112, for cooling racks 110, moves towards racks 110. In atleast one embodiment, second coolant from CDU 112 passes from on oneside of room manifold having tubing 118, to one side of rack 110 via rowmanifold 116, and through one side of this server tray via tubing 114.In at least one embodiment, spent or returned second coolant (or exitingsecond coolant carrying heat from computing components) exits out ofanother side of this server tray (such as enter left side of a rack andexit right side of this rack for a server tray after looping throughthis server tray or through components on this server tray). In at leastone embodiment, spent second coolant that exits this server tray or rack110 comes out of a different side (such as exiting side) of tubing 114and moves to a parallel, but also exiting side of row manifold 116. Inat least one embodiment, from row manifold 116, spent second coolantmoves in a parallel portion of room manifold 118 going in an oppositedirection than incoming second coolant (which may also be renewed secondcoolant), and towards CDU 112.

In at least one embodiment, spent second coolant exchanges its heat witha primary coolant in primary cooling loop 106 via CDU 112. In at leastone embodiment, spent second coolant is renewed (such as relativelycooled when compared to a temperature at spent second coolant stage) andready to be cycled back to through second cooling loop 108 to thesecomputing components. In at least one embodiment, various flow andtemperature control features in CDU 112 enable control of heat exchangedfrom spent second coolant or flow of second coolant in and out of CDU112. CDU 112 is also able to control a flow of primary coolant inprimary cooling loop 106.

In at least one embodiment, operation of components within a data centercan generate patterns of temperature variance and heat flow, such asillustrated by flow pattern 150 in FIG. 1B. In at least one embodiment,a cooling system may utilize one or more fluids (e.g., liquid or air) toattempt to manage temperature variations and levels across such acomputing environment. In at least one embodiment, fluid-based coolingof high-density servers can be used to manage sudden high heatvariations caused by changing computing loads or other such factors. Inat least one embodiment, as requirements are subject to change or tendto range from a minimum to a maximum of different cooling requirements,these requirements must be met in an economical manner, using anappropriate cooling system. In at least one embodiment, for moderate tohigh cooling requirements, liquid cooling system may be used. In atleast one embodiment, different cooling requirements also reflectdifferent heat features of this datacenter. In at least one embodiment,heat generated from these components, servers, and racks arecumulatively referred to as a heat feature or a cooling requirement asthis cooling requirement must address this heat feature entirely.

In at least one embodiment, temperature and air flow can vary within acomputing device as well, such as illustrated for computing device 180of FIG. 1C. In at least one embodiment, patterns of air flow 182 can bedetermined inside a computing device, here shown without at least a topcover to show flow among various components inside this device. Variousmodeling approaches can be used to determine air flow, powerdistribution, temperature variance, and other such aspects of such adevice during operation.

In at least one embodiment, a cooling system is disclosed that addressesheat features in associated computing or datacenter devices, such as ingraphics processing units (GPUs), in switches, in dual inline memorymodule (DIMMs), or central processing units (CPUs). Furthermore, in atleast one embodiment, an associated computing or datacenter device mayinclude a processing card having one or more GPUs, switches, or CPUsthereon. In at least one embodiment, each of these GPUs, switches, andCPUs may be a heat generating feature of this computing device. In atleast one embodiment, this GPU, CPU, or switch may have one or morecores, and each core may be a heat generating feature.

In at least one embodiment, such a cooling system can utilize one ormore neural networks with various systems, devices, components, boards,or cards within a computing environment to provide for predictions ofone or more future states of that environment. In at least oneembodiment, this can involve use of a neural networks computationalfluids dynamics (NNCFD) system that provides an ability to predictfuture state, such as temperatures at one or more locations at one ormore future periods of time, within a computing environment. In at leastone embodiment, these predictions can be inferences made using dataabout performance of data center equipment, as well as information suchas temperature, air flow, and pressure at various locations throughoutthis environment. In at least one embodiment, a control device caninclude or execute an NNCFD application that is able to collect variousthermal, fluid flow, power, and environmental data from variouslocations throughout a computing environment, such as a data center, aset of data centers, a server rack, or an individual server, among otherdevices and systems discussed and suggested herein. In at least oneembodiment, an NNCFD can be trained, with continued learning over time,to provide accurate predictive modeling with various agents running inequipment across this environment. In at least one embodiment, this mayinclude individual agents of one or more types, such as a board or ASICwith built in machine learning capability. In at least one embodiment,these individual agents can perform a task of adjusting aspects such ascooling (e.g., fan speed control or liquid flow control), powerdistribution (e.g., a power distribution unit (PDU) or uninterruptablepower supply (UPS)), and facility infrastructure (e.g., a chiller orcooling tower) control based on at least in part upon inferences fromthis predictive NNCFD to best account for any upcoming significant poweror thermal events ahead of time to smoothly navigate these events and,in many cases, prevent these events from occurring. In at least oneembodiment, an NNCFD application can provide for intelligent, predictivedata center building management system (BMS) or data centerinfrastructure management (DCIM). In at least one embodiment, such asystem can be designed using specific hardware that can enable machinelearning capability to be incorporated directly into various computingdevices or systems, such as Jetson boards from NVIDIA Corporation, whichcan utilize one or more graphics processing units (GPUs) on a board toefficiently provide for neural network-based inferencing andpredictions.

In at least one embodiment, sensors and other such devices can be usedto measure aspects such as air flow and temperature variation. In atleast one embodiment, data for these aspects can be used to model acurrent state of a computing environment, such as is illustrated fordata center 200 of FIG. 2A. In at least one embodiment, there are anumber of racks 202 or other groupings of computer servers, which eachinclude one or more fans or other such elements that can force air outof those servers. In at least one embodiment, this can be modeled as apattern 204 of air flow and associated temperatures. In at least oneembodiment, there may be various events, such as a period of unusuallyhigh compute load or an external heat event, that may result in anunacceptable state, as illustrated in view 250 of this data center inFIG. 2B. In at least one embodiment, darker shaded regions cancorrespond to regions of higher temperature, and it can be seen thatthere are concentrations of high temperatures in certain regions of FIG.2B. In at least one embodiment, this may result in temperatures thatexceed a maximum operating temperature threshold, and thus may result indamage to at least some of these computing device or relatedinfrastructure. In at least one embodiment, use of neural networks todynamically predict such occurrences or events in near real time, and beable to make adjustments proactively instead of reactively, can help toeither reduce an occurrence or extent of such an event, or at leastreduce an impact of such an event on these servers or relatedinfrastructure.

In at least one embodiment, neural networks can be trained to solvefluid mechanics and dynamics problems that might otherwise be solvedusing a set of complex equations. In at least one embodiment, such anequation is given by:

${{\frac{\partial}{\partial t}{\int_{V}{{\rho\phi}\;{dV}}}} + {\oint_{A}{{\rho\phi}\;{V \cdot {dA}}}}} = {{\oint_{A}{\Gamma_{\phi}{{\nabla\phi} \cdot {dA}}}} + {\int_{V}{S_{\phi}{dV}}}}$

where a sum of unsteady and convection components is equal to a sum ofdiffusion and generation components. In at least one embodiment, such anequation is a function of, for example, continuity, momentum, andenergy. In at least one embodiment, an objective for solving such anequation is to obtain fluid velocity (air or liquid), temperature, andpressure values at any point in a computing environment, at any currentor future point in time. In at least one embodiment, a neuralnetwork-based architecture can be used to solve such a computationalmechanics or physics problem. In at least one embodiment, this can beaccomplished using a point cloud representation for three-dimensional(3D) geometries, as well as multi-physics networks that maintain respectfor governing partial differential equations (PDEs). In at least oneembodiment, performance of such neural network-based modeling can beoptimized for various configurations, such as multi-node, multi-GPU,Automated Mixed Precision (AMP), or Accelerated Linear Algebra (XLA)configurations, such as those offered by NVIDIA Corporation. In at leastone embodiment, such a neural network can be trained using lossfunctions that account for aspects such as partial differentialequations (PDEs), boundary conditions (BCs), initial conditions (ICs),and environmental data. In at least one embodiment, such a network canbe trained using an unsupervised, physics-driven approach. In at leastone embodiment, there can be one model trained for each type ofequipment to be utilized, and updated network parameters can be utilizedas obtained over time from further training on additional data. In atleast one embodiment, an appropriate type of neural network can be usedthat is appropriate for equation solving and physics-based analysis, asmay include SIMNet from NVIDIA Corporation. In at least one embodiment,SIMNet provides a simulation toolkit based on physics and machinelearning. In at least one embodiment, SIMNet provides a framework tomodel partial differential equations (PDEs) along with boundary andinitial conditions. In at least one embodiment, flow problems can besolved in part by modeling a mass balance condition as a hard constraintas well as a global constraint, which can improve accuracy as well asconvergence characteristics. In at least one embodiment, formulti-physics problems spanning multiple domains, separate networks fordifferent physics with coupling at domain interfaces provides acceptableperformance as well.

In at least one embodiment, neural networks can be embedded in variousdevices to enable those devices to predict future state, and makeproactive adjustments based at least in part upon those predictions. Inat least one embodiment, this can include neural network enabled devicesto be installed directly in a liquid cooled server rack 300 asillustrated in FIG. 3A. In at least one embodiment, rack 300 can includea number of liquid cooled servers 302 or other such devices. In at leastone embodiment, a neural network enabled rack manifold 304 can beincluded in rack 300 to provide a flow of liquid into each liquid cooledserver 302 through an inlet value 308, and return liquid with heatremoved from that server 302 through an outlet value 310. In at leastone embodiment, rack manifold 304 can include a number of neuralnetwork-enabled secondary liquid distribution control boards 306. In atleast one embodiment, there can be one such control board 306 for eachinlet valve 308 and outlet valve 310 for each server. In at least oneembodiment, rack 300 can also include a neural network-enabled rackpower distribution unit (PDU) 312, which can include multiple outletsdesigned to distribute electric power to computers or networkingequipment within rack 300. In at least one embodiment, there can be aneural network-enabled power distribution control board 314 associatedwith each outlet of PDU 312. In at least one embodiment, sensors cancapture information about temperature, air flow, or other such aspectsof a computing environment internal and/or external to rack 300,including internal and/or external to any individual servers 302 locatedtherein. In at least one embodiment, fluid cooling can remove an amountof heat from servers 302, but due to factors such as varying load andexternal temperature fluctuations, temperatures at various locations maychange, and may reach or exceed temperature limits at which thesedevices can continue to operate correctly. In at least one embodiment,an attempt can be made to ensure that temperatures at specific locationsremain below an acceptable limit, where those locations may relate tojunction temperatures or core temperatures for a processor (e.g., a CPUor GPU), memory module, or power supply.

In at least one embodiment, neural networks in these control boards 306,314 can be tasked to predict values such as coolant flow or air flow, aswell as temperatures at these various locations at one or more futurepoints in time. In at least one embodiment, predictions from thesenetworks can be used by a respective board, manifold, PDU, or other suchdevice to make adjustments to ensure appropriate temperature and flowand keep this equipment functioning properly. In at least oneembodiment, these adjustments can include adjusting an amount of fluidor air flowing into, or out of, a server or device, as well as atemperature of that fluid or air. In at least one embodiment, this mayalso include adjusting an amount of power supplied to one or more ofthese devices. In at least one embodiment, these can include similaradjustments as would be made in other data centers, but can be madebased on future predictions instead of observed states, enabling such asystem to be proactive instead of reactive. In at least one embodiment,this monitoring can occur continually or at least at regular intervals,in order to ensure proper continued operation.

In at least one embodiment, each neural network in each type ofcomponent can be trained specifically for that type of component, andcan receive updated network parameters that may be generated as a resultof further training or continued learning. In at least one embodiment,inferences generated by individual devices can be shared with otherdevices in a data center as well, which can help to make more accuratepredictions. In at least one embodiment, each control board 306, 314 canbe a neural network enabled board, ASIC, or other such component. In atleast one embodiment, a control board can have components of a generalpurpose computer, including at least one processor 1014 and memory 1016with associated circuitry, such as described for computing device 1002in FIG. 10A. In at least one embodiment, a Jetson board from NVIDIACorporation can be used that is a complete system-on-module (SPM), withCPU, GPU, PMIC, DRAM, and flash storage included therein. In at leastone embodiment, such a module is also extensible to provide additionalfunctionality or capabilities. In at least one embodiment, each suchboard can function as a small artificial intelligence (AI) or machinelearning enabled computer. In at least one embodiment, multiple suchboards can be used in parallel, such as on rack manifold 304, in orderto concurrently process data from multiple high-resolution sensors. Inat least one embodiment, these various neural networks can makepredictions that can be shared across servers, a rack, or a data center,for example, in order to provide accurate predictions for variouslocations at one or more future points in time. In at least oneembodiment, appropriate adjustments can then be made as appropriate inorder to maintain temperatures and other parameters at appropriatelevels or values. In at least one embodiment, a liquid distributioncontrol board 306 may make predictions that can be used to adjust onlycontrols for a relevant flow value, or may share these prediction sothat other adjustments can be made as well based at least in part uponthese predictions. In at least one embodiment, a control system for adata center may collect predictions from these various boards ornetworks in order to make adjustments that are more appropriate for anoverall data center. In at least one embodiment, adjustments can then bemade at a server, rack, pod, data center, or other such level.

In at least one embodiment, a similar approach can be used with an aircooled server rack 350 as illustrated in FIG. 3B. In at least oneembodiment, a neural network-enabled rack PDU 354 can include a numberof neural network enabled power distribution control boards 356 tocontrol power distribution for a number of air cooled servers 352. In atleast one embodiment, local power distribution boards 356 can make localadjustments within rack 350, while external components in a respectivedata center may make adjustments to overall air flow or other suchaspects.

In at least one embodiment, a neural network enabled approach to liquidcooling of racks at a data center level can utilized components of asystem 400 illustrated in FIG. 4A. In at least one embodiment, anexternal cooling unit 402 can provide liquid at a determined temperatureto a data center cooling distribution unit 404. In at least oneembodiment, this unit 404 can include a set of neural network-enableddistribution unit control boards 406, which can perform inferencing andmake adjustments based upon inferred future values. In at least oneembodiment, this can include adjusting a temperature of liquid to bereceived from external cooling unit 402, or adjusting a flow of thatliquid to and/or from that external unit 402. In at least oneembodiment, this can also include adjusting flow into a manifold 408 fora row of liquid cooled racks 412, as well as flow into and out of thoseracks, as may be determined based at least in part upon inferences fromneural network-enabled control boards 410 built into row manifold 410.In at least one embodiment, there can be different levels of flow into,and out of, different liquid cooled racks. In at least one embodiment,there may also be different flows into individual servers in a rack,such as is described with respect to FIG. 3A. In at least oneembodiment, a similar approach can be used for an air-cooled data center450, as illustrated in FIG. 4B. In at least one embodiment, an externalcooling unit 452 can provide cooled air at a determined temperature to acooling distribution unit 454 that can have built in a number of neuralnetwork-enabled control boards 456, as may include computer room airconditioning (CRAC), computer room air handler (CRAH), SCHX, or in-rowcontrol board. In at least one embodiment, temperature and air flow canbe measured and predictions made for future values, at times such as 30seconds, one minute, five minutes, or an hour into our future, which canthen enable adjustments to be made to air temperature, air flow, powerdistribution, and other such aspects. In at least one embodiment, thesepredictions can be made using various types of input data, such asserver load, current temperature, current pressure, flow rate, powerconsumption, and other pertinent data for various locations within thatdata center.

In at least one embodiment, these neural network-enabled boards cancontrol an amount of air flow, supply, and return at various locations,such as where air is blown into a raised air space under racks ofservers, this air flow goes through these servers and then returns overthese servers. In at least one embodiment, these boards can be used topredict, control, and create an optimal solution for such a data centerto maintain desired operating conditions, with no loss of equipment ordowntime due to temperature issues or thermal events. In at least oneembodiment, this can be based on current and anticipated load, as wellas measured or detected network conditions. In at least one embodiment,this approach can be applied to other types of devices as well, as mayinclude UPS power supplies. In at least one embodiment, any device witha logic board or component can have this small neural network codeinjected for these and other such purposes. In at least one embodiment,such a network can tap into all equipment available in a unit or device,such that in a device such as a PDU a board can adjust or cut powercompletely to a server node, such as where a leak is detected orpredicted and relevant equipment may be subject to damage if operationcontinues. In at least one embodiment, operation can be terminated, orpower shut down, gracefully in order to avoid damage. In at least oneembodiment, executing programs may instead be terminated in order to atleast avoid a loss of data.

In at least one embodiment, a neural network can predict fluidtemperature at various locations in our future based on availableinformation. In at least one embodiment, this can include a snapshot ofcurrent information, or may include at least some data over a recentpast. In at least one embodiment, these predictions can then be used toadjust aspects such as fluid temperature or flow rate. In at least oneembodiment, a cooling distribution unit may adjust a flow of liquid orcoolant temperature. In at least one embodiment, such equipment can takethese predictions and be proactive in order to prevent any undesirableevents before they occur. In at least one embodiment, detection ofexternal events such as a forest fire may also be used to predict afuture significant increase in temperature in a data center, which maycause this data center to shut down all equipment gracefully and savedata, move data to another data center, or bring additional coolingsystems online to provide additional cooling as needed to prevent anexcessive internal heat event. In at least one embodiment, this can allbe performed before any increase in internal temperature is detected.

In at least one embodiment, a variety of sensors may provide informationabout a current state of a computing environment. In at least oneembodiment, this may include use of sensors such as temperature sensors,load sensors, flow sensors, or pressure sensors, which can beinstantaneously or historically gathered. In at least one embodiment,predictions made based on data from these sensors may be compared to oneor more thresholds, ranges, or other operational criteria to determinewhether any changes should be made. In at least one embodiment, this caninclude making an adjustment to prevent an unacceptable temperature riseat a specific location within a data center or other such environment.In at least one embodiment, this may include closing a valve orincreasing an amount of coolant flow, adjusting a coolant temperature,or sounding an alarm, among other such remedial actions.

In at least one embodiment, a process 500 for adjusting one or moreenvironmental control components can be performed as illustrated in FIG.5A. In at least one embodiment, environmental data is captured 502 forone or more locations in a computing environment. In at least oneembodiment, this can include data captured by sensors in a rack, server,cooling unit, or other such device. In at least one embodiment, at leastsome of this environmental data can be provided 504 as input to one ormore neural networks, as may be utilized by neural-network enabledboards or devices inside, or associated with, one or more devices orsystems in this environment. In at least one embodiment, one or morepredictions can be received 506 from these neural networks, where thosepredictions relate to a future value at one or more locations withinthat computing environment, where these values can relate toenvironmental aspects such as temperature, pressure, fluid flow, power,humidity, and so on. In at least one embodiment, these future values canbe compared 508 against one or more relevant thresholds or ranges. In atleast one embodiment, other values can be predicted and analyzed aswell, as may impact operation of computing devices within thatenvironment. A determination can be made 510 as to whether any of thesepredicted values exceed a maximum threshold, or otherwise fall outsidean allowable or specified range. If not, this process can continue. Ifone or more predicted values exceed such a threshold, or fall outsidesuch a range, then one or more environmental control components can beadjusted 512 to cause one or more of these future values to remain belowrelevant threshold(s), or within relevant range(s). In at least oneembodiment, this process can then continue, with adjustments being madebased at least in part upon future predictions. In at least oneembodiment, adjustments can be made even if a value such as temperaturewill not exceed such a threshold, but in an attempt to maintain adesired temperature or environmental condition.

In at least one embodiment, a process 550 for determining one or moretemperature control settings can be performed as illustrated in FIG. 5B.In at least one embodiment, input is received 552 relating toenvironmental parameters for one or more servers. In at least oneembodiment, this input can include data relating to temperature, airflow, pressure, or other such values at one or more locations. In atleast one embodiment, one or more future temperatures can be predicted554 using this input with one or more neural networks, such as topredict temperatures in or near these servers at one or more futurepoints in time. In at least one embodiment, one or more temperaturecontrol settings can then be determined 556 for these one or moreservers using these predicted future temperatures. In at least oneembodiment, this can include adjusting a fluid temperature or flow, orpower settings, in order to maintain a temperature within a desiredrange and avoid a temperature such as that predicted for one or morelocations.

Servers and Data Centers

The following figures set forth, without limitation, exemplary networkserver and datacenter based systems that can be used to implement atleast one embodiment.

FIG. 6 illustrates a distributed system 600, in accordance with at leastone embodiment. In at least one embodiment, distributed system 600includes one or more client computing devices 602, 604, 606, and 608,which are configured to execute and operate a client application such asa web browser, proprietary client, and/or variations thereof over one ormore network(s) 610. In at least one embodiment, server 612 may becommunicatively coupled with remote client computing devices 602, 604,606, and 608 via network 610.

In at least one embodiment, server 612 may be adapted to run one or moreservices or software applications such as services and applications thatmay manage session activity of single sign-on (SSO) access acrossmultiple datacenters. In at least one embodiment, server 612 may alsoprovide other services or software applications can include non-virtualand virtual environments. In at least one embodiment, these services maybe offered as web-based or cloud services or under a Software as aService (SaaS) model to users of client computing devices 602, 604, 606,and/or 608. In at least one embodiment, users operating client computingdevices 602, 604, 606, and/or 608 may in turn utilize one or more clientapplications to interact with server 612 to utilize services provided bythese components.

In at least one embodiment, software components 618, 620 and 622 ofsystem 600 are implemented on server 612. In at least one embodiment,one or more components of system 600 and/or services provided by thesecomponents may also be implemented by one or more of client computingdevices 602, 604, 606, and/or 608. In at least one embodiment, usersoperating client computing devices may then utilize one or more clientapplications to use services provided by these components. In at leastone embodiment, these components may be implemented in hardware,firmware, software, or combinations thereof. It should be appreciatedthat various different system configurations are possible, which may bedifferent from distributed system 600. The embodiment shown in FIG. 6 isthus at least one embodiment of a distributed system for implementing anembodiment system and is not intended to be limiting.

In at least one embodiment, client computing devices 602, 604, 606,and/or 608 may include various types of computing systems. In at leastone embodiment, a client computing device may include portable handhelddevices (e.g., an iPhone®, cellular telephone, an iPad®, computingtablet, a personal digital assistant (PDA)) or wearable devices (e.g., aGoogle Glass® head mounted display), running software such as MicrosoftWindows Mobile®, and/or a variety of mobile operating systems such asiOS, Windows Phone, Android, BlackBerry 10, Palm OS, and/or variationsthereof. In at least one embodiment, devices may support variousapplications such as various Internet-related apps, e-mail, shortmessage service (SMS) applications, and may use various othercommunication protocols. In at least one embodiment, client computingdevices may also include general purpose personal computers including,by way of at least one embodiment, personal computers and/or laptopcomputers running various versions of Microsoft Windows®, AppleMacintosh®, and/or Linux operating systems.

In at least one embodiment, client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation a variety ofGNU/Linux operating systems, such as Google Chrome OS. In at least oneembodiment, client computing devices may also include electronic devicessuch as a thin-client computer, an Internet-enabled gaming system (e.g.,a Microsoft Xbox gaming console with or without a Kinect® gesture inputdevice), and/or a personal messaging device, capable of communicatingover network(s) 610. Although distributed system 600 in FIG. 6 is shownwith four client computing devices, any number of client computingdevices may be supported. Other devices, such as devices with sensors,etc., may interact with server 612.

In at least one embodiment, network(s) 610 in distributed system 600 maybe any type of network that can support data communications using any ofa variety of available protocols, including without limitation TCP/IP(transmission control protocol/Internet protocol), SNA (systems networkarchitecture), IPX (Internet packet exchange), AppleTalk, and/orvariations thereof. In at least one embodiment, network(s) 610 can be alocal area network (LAN), networks based on Ethernet, Token-Ring, awide-area network, Internet, a virtual network, a virtual privatenetwork (VPN), an intranet, an extranet, a public switched telephonenetwork (PSTN), an infra-red network, a wireless network (e.g., anetwork operating under any of the Institute of Electrical andElectronics (IEEE) 802.11 suite of protocols, Bluetooth®, and/or anyother wireless protocol), and/or any combination of these and/or othernetworks.

In at least one embodiment, server 612 may be composed of one or moregeneral purpose computers, specialized server computers (including, byway of at least one embodiment, PC (personal computer) servers, UNIX®servers, mid-range servers, mainframe computers, rack-mounted servers,etc.), server farms, server clusters, or any other appropriatearrangement and/or combination. In at least one embodiment, server 612can include one or more virtual machines running virtual operatingsystems, or other computing architectures involving virtualization. Inat least one embodiment, one or more flexible pools of logical storagedevices can be virtualized to maintain virtual storage devices for aserver. In at least one embodiment, virtual networks can be controlledby server 612 using software defined networking. In at least oneembodiment, server 612 may be adapted to run one or more services orsoftware applications.

In at least one embodiment, server 612 may run any operating system, aswell as any commercially available server operating system. In at leastone embodiment, server 612 may also run any of a variety of additionalserver applications and/or mid-tier applications, including HTTP(hypertext transport protocol) servers, FTP (file transfer protocol)servers, CGI (common gateway interface) servers, JAVA® servers, databaseservers, and/or variations thereof. In at least one embodiment,exemplary database servers include without limitation those commerciallyavailable from Oracle, Microsoft, Sybase, IBM (International BusinessMachines), and/or variations thereof.

In at least one embodiment, server 612 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 602, 604, 606, and 608.In at least one embodiment, data feeds and/or event updates may include,but are not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and/or variations thereof. In at least one embodiment,server 612 may also include one or more applications to display datafeeds and/or real-time events via one or more display devices of clientcomputing devices 602, 604, 606, and 608.

In at least one embodiment, distributed system 600 may also include oneor more databases 614 and 616. In at least one embodiment, databases mayprovide a mechanism for storing information such as user interactionsinformation, usage patterns information, adaptation rules information,and other information. In at least one embodiment, databases 614 and 616may reside in a variety of locations. In at least one embodiment, one ormore of databases 614 and 616 may reside on a non-transitory storagemedium local to (and/or resident in) server 612. In at least oneembodiment, databases 614 and 616 may be remote from server 612 and incommunication with server 612 via a network-based or dedicatedconnection. In at least one embodiment, databases 614 and 616 may residein a storage-area network (SAN). In at least one embodiment, anynecessary files for performing functions attributed to server 612 may bestored locally on server 612 and/or remotely, as appropriate. In atleast one embodiment, databases 614 and 616 may include relationaldatabases, such as databases that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 7 illustrates an exemplary datacenter 700, in accordance with atleast one embodiment. In at least one embodiment, datacenter 700includes, without limitation, a datacenter infrastructure layer 710, aframework layer 720, a software layer 730 and an application layer 740.

In at least one embodiment, as shown in FIG. 7, datacenterinfrastructure layer 710 may include a resource orchestrator 712,grouped computing resources 714, and node computing resources (“nodeC.R.s”) 716(1)-716(N), where “N” represents any whole, positive integer.In at least one embodiment, node C.R.s 716(1)-716(N) may include, butare not limited to, any number of central processing units (“CPUs”) orother processors (including accelerators, field programmable gate arrays(“FPGAs”), graphics processors, etc.), memory devices (e.g., dynamicread-only memory), storage devices (e.g., solid state or disk drives),network input/output (“NW I/O”) devices, network switches, virtualmachines (“VMs”), power modules, and cooling modules, etc. In at leastone embodiment, one or more node C.R.s from among node C.R.s716(1)-716(N) may be a server having one or more of above-mentionedcomputing resources.

In at least one embodiment, grouped computing resources 714 may includeseparate groupings of node C.R.s housed within one or more racks (notshown), or many racks housed in datacenters at various geographicallocations (also not shown). Separate groupings of node C.R.s withingrouped computing resources 714 may include grouped compute, network,memory or storage resources that may be configured or allocated tosupport one or more workloads. In at least one embodiment, several nodeC.R.s including CPUs or processors may grouped within one or more racksto provide compute resources to support one or more workloads. In atleast one embodiment, one or more racks may also include any number ofpower modules, cooling modules, and network switches, in anycombination.

In at least one embodiment, resource orchestrator 712 may configure orotherwise control one or more node C.R.s 716(1)-716(N) and/or groupedcomputing resources 714. In at least one embodiment, resourceorchestrator 712 may include a software design infrastructure (“SDI”)management entity for datacenter 700. In at least one embodiment,resource orchestrator 712 may include hardware, software or somecombination thereof.

In at least one embodiment, as shown in FIG. 7, framework layer 720includes, without limitation, a job scheduler 732, a configurationmanager 734, a resource manager 736 and a distributed file system 738.In at least one embodiment, framework layer 720 may include a frameworkto support software 752 of software layer 730 and/or one or moreapplication(s) 742 of application layer 740. In at least one embodiment,software 752 or application(s) 742 may respectively include web-basedservice software or applications, such as those provided by Amazon WebServices, Google Cloud and Microsoft Azure. In at least one embodiment,framework layer 720 may be, but is not limited to, a type of free andopen-source software web application framework such as Apache Spark™(hereinafter “Spark”) that may utilize distributed file system 738 forlarge-scale data processing (e.g., “big data”). In at least oneembodiment, job scheduler 732 may include a Spark driver to facilitatescheduling of workloads supported by various layers of datacenter 700.In at least one embodiment, configuration manager 734 may be capable ofconfiguring different layers such as software layer 730 and frameworklayer 720, including Spark and distributed file system 738 forsupporting large-scale data processing. In at least one embodiment,resource manager 736 may be capable of managing clustered or groupedcomputing resources mapped to or allocated for support of distributedfile system 738 and job scheduler 732. In at least one embodiment,clustered or grouped computing resources may include grouped computingresource 714 at datacenter infrastructure layer 710. In at least oneembodiment, resource manager 736 may coordinate with resourceorchestrator 712 to manage these mapped or allocated computingresources.

In at least one embodiment, software 752 included in software layer 730may include software used by at least portions of node C.R.s716(1)-716(N), grouped computing resources 714, and/or distributed filesystem 738 of framework layer 720. One or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 742 included in applicationlayer 740 may include one or more types of applications used by at leastportions of node C.R.s 716(1)-716(N), grouped computing resources 714,and/or distributed file system 738 of framework layer 720. In at leastone or more types of applications may include, without limitation, CUDAapplications, 5G network applications, artificial intelligenceapplication, datacenter applications, and/or variations thereof.

In at least one embodiment, any of configuration manager 734, resourcemanager 736, and resource orchestrator 712 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. In at least oneembodiment, self-modifying actions may relieve a datacenter operator ofdatacenter 700 from making possibly bad configuration decisions andpossibly avoiding underutilized and/or poor performing portions of adatacenter.

FIG. 8 illustrates a client-server network 804 formed by a plurality ofnetwork server computers 802 which are interlinked, in accordance withat least one embodiment. In at least one embodiment, each network servercomputer 802 stores data accessible to other network server computers802 and to client computers 806 and networks 808 which link into a widearea network 804. In at least one embodiment, configuration of aclient-server network 804 may change over time as client computers 806and one or more networks 808 connect and disconnect from a network 804,and as one or more trunk line server computers 802 are added or removedfrom a network 804. In at least one embodiment, when a client computer806 and a network 808 are connected with network server computers 802,client-server network includes such client computer 806 and network 808.In at least one embodiment, the term computer includes any device ormachine capable of accepting data, applying prescribed processes todata, and supplying results of processes.

In at least one embodiment, client-server network 804 stores informationwhich is accessible to network server computers 802, remote networks 808and client computers 806. In at least one embodiment, network servercomputers 802 are formed by main frame computers minicomputers, and/ormicrocomputers having one or more processors each. In at least oneembodiment, server computers 802 are linked together by wired and/orwireless transfer media, such as conductive wire, fiber optic cable,and/or microwave transmission media, satellite transmission media orother conductive, optic or electromagnetic wave transmission media. Inat least one embodiment, client computers 806 access a network servercomputer 802 by a similar wired or a wireless transfer medium. In atleast one embodiment, a client computer 806 may link into aclient-server network 804 using a modem and a standard telephonecommunication network. In at least one embodiment, alternative carriersystems such as cable and satellite communication systems also may beused to link into client-server network 804. In at least one embodiment,other private or time-shared carrier systems may be used. In at leastone embodiment, network 804 is a global information network, such as theInternet. In at least one embodiment, network is a private intranetusing similar protocols as the Internet, but with added securitymeasures and restricted access controls. In at least one embodiment,network 804 is a private, or semi-private network using proprietarycommunication protocols.

In at least one embodiment, client computer 806 is any end usercomputer, and may also be a mainframe computer, mini-computer ormicrocomputer having one or more microprocessors. In at least oneembodiment, server computer 802 may at times function as a clientcomputer accessing another server computer 802. In at least oneembodiment, remote network 808 may be a local area network, a networkadded into a wide area network through an independent service provider(ISP) for the Internet, or another group of computers interconnected bywired or wireless transfer media having a configuration which is eitherfixed or changing over time. In at least one embodiment, clientcomputers 806 may link into and access a network 804 independently orthrough a remote network 808.

FIG. 9 illustrates a computer network 908 connecting one or morecomputing machines, in accordance with at least one embodiment. In atleast one embodiment, network 908 may be any type of electronicallyconnected group of computers including, for instance, the followingnetworks: Internet, Intranet, Local Area Networks (LAN), Wide AreaNetworks (WAN) or an interconnected combination of these network types.In at least one embodiment, connectivity within a network 908 may be aremote modem, Ethernet (IEEE 802.3), Token Ring (IEEE 802.5), FiberDistributed Datalink Interface (FDDI), Asynchronous Transfer Mode (ATM),or any other communication protocol. In at least one embodiment,computing devices linked to a network may be desktop, server, portable,handheld, set-top box, personal digital assistant (PDA), a terminal, orany other desired type or configuration. In at least one embodiment,depending on their functionality, network connected devices may varywidely in processing power, internal memory, and other performanceaspects.

In at least one embodiment, communications within a network and to orfrom computing devices connected to a network may be either wired orwireless. In at least one embodiment, network 908 may include, at leastin part, the world-wide public Internet which generally connects aplurality of users in accordance with a client-server model inaccordance with a transmission control protocol/internet protocol(TCP/IP) specification. In at least one embodiment, client-servernetwork is a dominant model for communicating between two computers. Inat least one embodiment, a client computer (“client”) issues one or morecommands to a server computer (“server”). In at least one embodiment,server fulfills client commands by accessing available network resourcesand returning information to a client pursuant to client commands. In atleast one embodiment, client computer systems and network resourcesresident on network servers are assigned a network address foridentification during communications between elements of a network. Inat least one embodiment, communications from other network connectedsystems to servers will include a network address of a relevantserver/network resource as part of communication so that an appropriatedestination of a data/request is identified as a recipient. In at leastone embodiment, when a network 908 comprises the global Internet, anetwork address is an IP address in a TCP/IP format which may, at leastin part, route data to an e-mail account, a website, or other Internettool resident on a server. In at least one embodiment, information andservices which are resident on network servers may be available to a webbrowser of a client computer through a domain name (e.g. www.site.com)which maps to an IP address of a network server.

In at least one embodiment, a plurality of clients 902, 904, and 906 areconnected to a network 908 via respective communication links. In atleast one embodiment, each of these clients may access a network 908 viaany desired form of communication, such as via a dial-up modemconnection, cable link, a digital subscriber line (DSL), wireless orsatellite link, or any other form of communication. In at least oneembodiment, each client may communicate using any machine that iscompatible with a network 908, such as a personal computer (PC), workstation, dedicated terminal, personal data assistant (PDA), or othersimilar equipment. In at least one embodiment, clients 902, 904, and 906may or may not be located in a same geographical area.

In at least one embodiment, a plurality of servers 910, 912, and 914 areconnected to a network 918 to serve clients that are in communicationwith a network 918. In at least one embodiment, each server is typicallya powerful computer or device that manages network resources andresponds to client commands. In at least one embodiment, servers includecomputer readable data storage media such as hard disk drives and RAMmemory that store program instructions and data. In at least oneembodiment, servers 910, 912, 914 run application programs that respondto client commands. In at least one embodiment, server 910 may run a webserver application for responding to client requests for HTML pages andmay also run a mail server application for receiving and routingelectronic mail. In at least one embodiment, other application programs,such as an FTP server or a media server for streaming audio/video datato clients may also be running on a server 910. In at least oneembodiment, different servers may be dedicated to performing differenttasks. In at least one embodiment, server 910 may be a dedicated webserver that manages resources relating to web sites for various users,whereas a server 912 may be dedicated to provide electronic mail (email)management. In at least one embodiment, other servers may be dedicatedfor media (audio, video, etc.), file transfer protocol (FTP), or acombination of any two or more services that are typically available orprovided over a network. In at least one embodiment, each server may bein a location that is the same as or different from that of otherservers. In at least one embodiment, there may be multiple servers thatperform mirrored tasks for users, thereby relieving congestion orminimizing traffic directed to and from a single server. In at least oneembodiment, servers 910, 912, 914 are under control of a web hostingprovider in a business of maintaining and delivering third party contentover a network 918.

In at least one embodiment, web hosting providers deliver services totwo different types of clients. In at least one embodiment, one type,which may be referred to as a browser, requests content from servers910, 912, 914 such as web pages, email messages, video clips, etc. In atleast one embodiment, a second type, which may be referred to as a user,hires a web hosting provider to maintain a network resource such as aweb site, and to make it available to browsers. In at least oneembodiment, users contract with a web hosting provider to make memoryspace, processor capacity, and communication bandwidth available fortheir desired network resource in accordance with an amount of serverresources a user desires to utilize.

In at least one embodiment, in order for a web hosting provider toprovide services for both of these clients, application programs whichmanage a network resources hosted by servers must be properlyconfigured. In at least one embodiment, program configuration processinvolves defining a set of parameters which control, at least in part,an application program's response to browser requests and which alsodefine, at least in part, a server resources available to a particularuser.

In one embodiment, an intranet server 916 is in communication with anetwork 908 via a communication link. In at least one embodiment,intranet server 916 is in communication with a server manager 918. In atleast one embodiment, server manager 918 comprises a database of anapplication program configuration parameters which are being utilized inservers 910, 912, 914. In at least one embodiment, users modify adatabase 920 via an intranet 916, and a server manager 918 interactswith servers 910, 912, 914 to modify application program parameters sothat they match a content of a database. In at least one embodiment, auser logs onto an intranet server 916 by connecting to an intranet 916via computer 902 and entering authentication information, such as ausername and password.

In at least one embodiment, when a user wishes to sign up for newservice or modify an existing service, an intranet server 916authenticates a user and provides a user with an interactive screendisplay/control panel that allows a user to access configurationparameters for a particular application program. In at least oneembodiment, a user is presented with a number of modifiable text boxesthat describe aspects of a configuration of a user's web site or othernetwork resource. In at least one embodiment, if a user desires toincrease memory space reserved on a server for its web site, a user isprovided with a field in which a user specifies a desired memory space.In at least one embodiment, in response to receiving this information,an intranet server 916 updates a database 920. In at least oneembodiment, server manager 918 forwards this information to anappropriate server, and a new parameter is used during applicationprogram operation. In at least one embodiment, an intranet server 916 isconfigured to provide users with access to configuration parameters ofhosted network resources (e.g., web pages, email, FTP sites, mediasites, etc.), for which a user has contracted with a web hosting serviceprovider.

FIG. 10A illustrates a networked computer system 1000A, in accordancewith at least one embodiment. In at least one embodiment, networkedcomputer system 1000A comprises a plurality of nodes or personalcomputers (“PCs”) 1002, 1018, 1020. In at least one embodiment, personalcomputer or node 1002 comprises a processor 1014, memory 1016, videocamera 1004, microphone 1006, mouse 1008, speakers 1010, and monitor1012. In at least one embodiment, PCs 1002, 1018, 1020 may each run oneor more desktop servers of an internal network within a given company,for instance, or may be servers of a general network not limited to aspecific environment. In at least one embodiment, there is one serverper PC node of a network, so that each PC node of a network represents aparticular network server, having a particular network URL address. Inat least one embodiment, each server defaults to a default web page forthat server's user, which may itself contain embedded URLs pointing tofurther subpages of that user on that server, or to other servers orpages on other servers on a network.

In at least one embodiment, nodes 1002, 1018, 1020 and other nodes of anetwork are interconnected via medium 1022. In at least one embodiment,medium 1022 may be, a communication channel such as an IntegratedServices Digital Network (“ISDN”). In at least one embodiment, variousnodes of a networked computer system may be connected through a varietyof communication media, including local area networks (“LANs”),plain-old telephone lines (“POTS”), sometimes referred to as publicswitched telephone networks (“PSTN”), and/or variations thereof. In atleast one embodiment, various nodes of a network may also constitutecomputer system users inter-connected via a network such as theInternet. In at least one embodiment, each server on a network (runningfrom a particular node of a network at a given instance) has a uniqueaddress or identification within a network, which may be specifiable interms of an URL.

In at least one embodiment, a plurality of multi-point conferencingunits (“MCUs”) may thus be utilized to transmit data to and from variousnodes or “endpoints” of a conferencing system. In at least oneembodiment, nodes and/or MCUs may be interconnected via an ISDN link orthrough a local area network (“LAN”), in addition to various othercommunications media such as nodes connected through the Internet. In atleast one embodiment, nodes of a conferencing system may, in general, beconnected directly to a communications medium such as a LAN or throughan MCU, and that a conferencing system may comprise other nodes orelements such as routers, servers, and/or variations thereof.

In at least one embodiment, processor 1014 is a general-purposeprogrammable processor. In at least one embodiment, processors of nodesof networked computer system 1000A may also be special-purpose videoprocessors. In at least one embodiment, various peripherals andcomponents of a node such as those of node 1002 may vary from those ofother nodes. In at least one embodiment, node 1018 and node 1020 may beconfigured identically to or differently than node 1002. In at least oneembodiment, a node may be implemented on any suitable computer system inaddition to PC systems.

FIG. 10B illustrates a networked computer system 1000B, in accordancewith at least one embodiment. In at least one embodiment, system 1000Billustrates a network such as LAN 1024, which may be used tointerconnect a variety of nodes that may communicate with each other. Inat least one embodiment, attached to LAN 1024 are a plurality of nodessuch as PC nodes 1026, 1028, 1030. In at least one embodiment, a nodemay also be connected to the LAN via a network server or other means. Inat least one embodiment, system 1000B comprises other types of nodes orelements, for at least one embodiment including routers, servers, andnodes.

FIG. 10C illustrates a networked computer system 1000C, in accordancewith at least one embodiment. In at least one embodiment, system 1000Cillustrates a WWW system having communications across a backbonecommunications network such as Internet 1032, which may be used tointerconnect a variety of nodes of a network. In at least oneembodiment, WWW is a set of protocols operating on top of the Internet,and allows a graphical interface system to operate thereon for accessinginformation through the Internet. In at least one embodiment, attachedto Internet 1032 in WWW are a plurality of nodes such as PCs 1040, 1042,1044. In at least one embodiment, a node is interfaced to other nodes ofWWW through a WWW HTTP server such as servers 1034, 1036. In at leastone embodiment, PC 1044 may be a PC forming a node of network 1032 anditself running its server 1036, although PC 1044 and server 1036 areillustrated separately in FIG. 10C for illustrative purposes.

In at least one embodiment, WWW is a distributed type of application,characterized by WWW HTTP, WWW's protocol, which runs on top of theInternet's transmission control protocol/Internet protocol (“TCP/IP”).In at least one embodiment, WWW may thus be characterized by a set ofprotocols (i.e., HTTP) running on the Internet as its “backbone.”

In at least one embodiment, a web browser is an application running on anode of a network that, in WWW-compatible type network systems, allowsusers of a particular server or node to view such information and thusallows a user to search graphical and text-based files that are linkedtogether using hypertext links that are embedded in documents or filesavailable from servers on a network that understand HTTP. In at leastone embodiment, when a given web page of a first server associated witha first node is retrieved by a user using another server on a networksuch as the Internet, a document retrieved may have various hypertextlinks embedded therein and a local copy of a page is created local to aretrieving user. In at least one embodiment, when a user clicks on ahypertext link, locally-stored information related to a selectedhypertext link is typically sufficient to allow a user's machine to opena connection across the Internet to a server indicated by a hypertextlink.

In at least one embodiment, more than one user may be coupled to eachHTTP server, through a LAN such as LAN 1038 as illustrated with respectto WWW HTTP server 1034. In at least one embodiment, system 1000C mayalso comprise other types of nodes or elements. In at least oneembodiment, a WWW HTTP server is an application running on a machine,such as a PC. In at least one embodiment, each user may be considered tohave a unique “server,” as illustrated with respect to PC 1044. In atleast one embodiment, a server may be considered to be a server such asWWW HTTP server 1034, which provides access to a network for a LAN orplurality of nodes or plurality of LANs. In at least one embodiment,there are a plurality of users, each having a desktop PC or node of anetwork, each desktop PC potentially establishing a server for a userthereof. In at least one embodiment, each server is associated with aparticular network address or URL, which, when accessed, provides adefault web page for that user. In at least one embodiment, a web pagemay contain further links (embedded URLs) pointing to further subpagesof that user on that server, or to other servers on a network or topages on other servers on a network.

Cloud Computing and Services

The following figures set forth, without limitation, exemplarycloud-based systems that can be used to implement at least oneembodiment.

In at least one embodiment, cloud computing is a style of computing inwhich dynamically scalable and often virtualized resources are providedas a service over the Internet. In at least one embodiment, users neednot have knowledge of, expertise in, or control over technologyinfrastructure, which can be referred to as “in the cloud,” thatsupports them. In at least one embodiment, cloud computing incorporatesinfrastructure as a service, platform as a service, software as aservice, and other variations that have a common theme of reliance onthe Internet for satisfying computing needs of users. In at least oneembodiment, a typical cloud deployment, such as in a private cloud(e.g., enterprise network), or a datacenter (DC) in a public cloud(e.g., Internet) can consist of thousands of servers (or alternatively,VMs), hundreds of Ethernet, Fiber Channel or Fiber Channel over Ethernet(FCoE) ports, switching and storage infrastructure, etc. In at least oneembodiment, cloud can also consist of network services infrastructurelike IPsec VPN hubs, firewalls, load balancers, wide area network (WAN)optimizers etc. In at least one embodiment, remote subscribers canaccess cloud applications and services securely by connecting via a VPNtunnel, such as an IPsec VPN tunnel.

In at least one embodiment, cloud computing is a model for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g., networks, servers, storage, applications, andservices) that can be rapidly provisioned and released with minimalmanagement effort or service provider interaction.

In at least one embodiment, cloud computing is characterized byon-demand self-service, in which a consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human inter-action with eachservice's provider. In at least one embodiment, cloud computing ischaracterized by broad network access, in which capabilities areavailable over a network and accessed through standard mechanisms thatpromote use by heterogeneous thin or thick client platforms (e.g.,mobile phones, laptops, and PDAs). In at least one embodiment, cloudcomputing is characterized by resource pooling, in which a provider'scomputing resources are pooled to serve multiple consumers using amulti-tenant model, with different physical and virtual resourcesdynamically as-signed and reassigned according to consumer demand. In atleast one embodiment, there is a sense of location independence in thata customer generally has no control or knowledge over an exact locationof provided resources, but may be able to specify location at a higherlevel of abstraction (e.g., country, state, or datacenter).

In at least one embodiment, resources include storage, processing,memory, network bandwidth, and virtual machines. In at least oneembodiment, cloud computing is characterized by rapid elasticity, inwhich capabilities can be rapidly and elastically provisioned, in somecases automatically, to quickly scale out and rapidly released toquickly scale in. In at least one embodiment, to a consumer,capabilities available for provisioning often appear to be unlimited andcan be purchased in any quantity at any time. In at least oneembodiment, cloud computing is characterized by measured service, inwhich cloud systems automatically control and optimize resource use byleveraging a metering capability at some level of abstractionappropriate to a type of service (e.g., storage, processing, bandwidth,and active user accounts). In at least one embodiment, resource usagecan be monitored, controlled, and reported providing transparency forboth a provider and consumer of a utilized service.

In at least one embodiment, cloud computing may be associated withvarious services. In at least one embodiment, cloud Software as aService (SaaS) may refer to as service in which a capability provided toa consumer is to use a provider's applications running on a cloudinfrastructure. In at least one embodiment, applications are accessiblefrom various client devices through a thin client interface such as aweb browser (e.g., web-based email). In at least one embodiment,consumer does not manage or control underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with a possible exception oflimited user-specific application configuration settings.

In at least one embodiment, cloud Platform as a Service (PaaS) may referto a service in which a capability provided to a consumer is to deployonto cloud infrastructure consumer-created or acquired applicationscreated using programming languages and tools supported by a provider.In at least one embodiment, consumer does not manage or controlunderlying cloud infrastructure including networks, servers, operatingsystems, or storage, but has control over deployed applications andpossibly application hosting environment configurations.

In at least one embodiment, cloud Infrastructure as a Service (IaaS) mayrefer to a service in which a capability provided to a consumer is toprovision processing, storage, networks, and other fundamental computingresources where a consumer is able to deploy and run arbitrary software,which can include operating systems and applications. In at least oneembodiment, consumer does not manage or control underlying cloudinfrastructure, but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

In at least one embodiment, cloud computing may be deployed in variousways. In at least one embodiment, a private cloud may refer to a cloudinfrastructure that is operated solely for an organization. In at leastone embodiment, a private cloud may be managed by an organization or athird party and may exist on-premises or off-premises. In at least oneembodiment, a community cloud may refer to a cloud infrastructure thatis shared by several organizations and supports a specific communitythat has shared concerns (e.g., mission, security requirements, policy,and compliance considerations). In at least one embodiment, a communitycloud may be managed by organizations or a third party and may existon-premises or off-premises. In at least one embodiment, a public cloudmay refer to a cloud infrastructure that is made available to a generalpublic or a large industry group and is owned by an organizationproviding cloud services. In at least one embodiment, a hybrid cloud mayrefer to a cloud infrastructure is a composition of two or more clouds(private, community, or public) that remain unique entities, but arebound together by standardized or proprietary technology that enablesdata and application portability (e.g., cloud bursting forload-balancing between clouds). In at least one embodiment, a cloudcomputing environment is service oriented with a focus on statelessness,low coupling, modularity, and semantic interoperability.

FIG. 11 illustrates one or more components of a system environment 1100in which services may be offered as third party network services, inaccordance with at least one embodiment. In at least one embodiment, athird party network may be referred to as a cloud, cloud network, cloudcomputing network, and/or variations thereof. In at least oneembodiment, system environment 1100 includes one or more clientcomputing devices 1104, 1106, and 1108 that may be used by users tointeract with a third party network infrastructure system 1102 thatprovides third party network services, which may be referred to as cloudcomputing services. In at least one embodiment, third party networkinfrastructure system 1102 may comprise one or more computers and/orservers.

It should be appreciated that third party network infrastructure system1102 depicted in FIG. 11 may have other components than those depicted.Further, FIG. 11 depicts an embodiment of a third party networkinfrastructure system. In at least one embodiment, third party networkinfrastructure system 1102 may have more or fewer components thandepicted in FIG. 11, may combine two or more components, or may have adifferent configuration or arrangement of components.

In at least one embodiment, client computing devices 1104, 1106, and1108 may be configured to operate a client application such as a webbrowser, a proprietary client application, or some other application,which may be used by a user of a client computing device to interactwith third party network infrastructure system 1102 to use servicesprovided by third party network infrastructure system 1102. Althoughexemplary system environment 1100 is shown with three client computingdevices, any number of client computing devices may be supported. In atleast one embodiment, other devices such as devices with sensors, etc.may interact with third party network infrastructure system 1102. In atleast one embodiment, network(s) 1110 may facilitate communications andexchange of data between client computing devices 1104, 1106, and 1108and third party network infrastructure system 1102.

In at least one embodiment, services provided by third party networkinfrastructure system 1102 may include a host of services that are madeavailable to users of a third party network infrastructure system ondemand. In at least one embodiment, various services may also be offeredincluding without limitation online data storage and backup solutions,Web-based e-mail services, hosted office suites and documentcollaboration services, database management and processing, managedtechnical support services, and/or variations thereof. In at least oneembodiment, services provided by a third party network infrastructuresystem can dynamically scale to meet needs of its users.

In at least one embodiment, a specific instantiation of a serviceprovided by third party network infrastructure system 1102 may bereferred to as a “service instance.” In at least one embodiment, ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a third party network serviceprovider's system is referred to as a “third party network service.” Inat least one embodiment, in a public third party network environment,servers and systems that make up a third party network serviceprovider's system are different from a customer's own on-premisesservers and systems. In at least one embodiment, a third party networkservice provider's system may host an application, and a user may, via acommunication network such as the Internet, on demand, order and use anapplication.

In at least one embodiment, a service in a computer network third partynetwork infrastructure may include protected computer network access tostorage, a hosted database, a hosted web server, a software application,or other service provided by a third party network vendor to a user. Inat least one embodiment, a service can include password-protected accessto remote storage on a third party network through the Internet. In atleast one embodiment, a service can include a web service-based hostedrelational database and a script-language middleware engine for privateuse by a networked developer. In at least one embodiment, a service caninclude access to an email software application hosted on a third partynetwork vendor's web site.

In at least one embodiment, third party network infrastructure system1102 may include a suite of applications, middleware, and databaseservice offerings that are delivered to a customer in a self-service,subscription-based, elastically scalable, reliable, highly available,and secure manner. In at least one embodiment, third party networkinfrastructure system 1102 may also provide “big data” relatedcomputation and analysis services. In at least one embodiment, term “bigdata” is generally used to refer to extremely large data sets that canbe stored and manipulated by analysts and researchers to visualize largeamounts of data, detect trends, and/or otherwise interact with data. Inat least one embodiment, big data and related applications can be hostedand/or manipulated by an infrastructure system on many levels and atdifferent scales. In at least one embodiment, tens, hundreds, orthousands of processors linked in parallel can act upon such data inorder to present it or simulate external forces on data or what itrepresents. In at least one embodiment, these data sets can involvestructured data, such as that organized in a database or otherwiseaccording to a structured model, and/or unstructured data (e.g., emails,images, data blobs (binary large objects), web pages, complex eventprocessing). In at least one embodiment, by leveraging an ability of anembodiment to relatively quickly focus more (or fewer) computingresources upon an objective, a third party network infrastructure systemmay be better available to carry out tasks on large data sets based ondemand from a business, government agency, research organization,private individual, group of like-minded individuals or organizations,or other entity.

In at least one embodiment, third party network infrastructure system1102 may be adapted to automatically provision, manage and track acustomer's subscription to services offered by third party networkinfrastructure system 1102. In at least one embodiment, third partynetwork infrastructure system 1102 may provide third party networkservices via different deployment models. In at least one embodiment,services may be provided under a public third party network model inwhich third party network infrastructure system 1102 is owned by anorganization selling third party network services and services are madeavailable to a general public or different industry enterprises. In atleast one embodiment, services may be provided under a private thirdparty network model in which third party network infrastructure system1102 is operated solely for a single organization and may provideservices for one or more entities within an organization. In at leastone embodiment, third party network services may also be provided undera community third party network model in which third party networkinfrastructure system 1102 and services provided by third party networkinfrastructure system 1102 are shared by several organizations in arelated community. In at least one embodiment, third party networkservices may also be provided under a hybrid third party network model,which is a combination of two or more different models.

In at least one embodiment, services provided by third party networkinfrastructure system 1102 may include one or more services providedunder Software as a Service (SaaS) category, Platform as a Service(PaaS) category, Infrastructure as a Service (IaaS) category, or othercategories of services including hybrid services. In at least oneembodiment, a customer, via a subscription order, may order one or moreservices provided by third party network infrastructure system 1102. Inat least one embodiment, third party network infrastructure system 1102then performs processing to provide services in a customer'ssubscription order.

In at least one embodiment, services provided by third party networkinfrastructure system 1102 may include, without limitation, applicationservices, platform services and infrastructure services. In at least oneembodiment, application services may be provided by a third partynetwork infrastructure system via a SaaS platform. In at least oneembodiment, SaaS platform may be configured to provide third partynetwork services that fall under a SaaS category. In at least oneembodiment, SaaS platform may provide capabilities to build and delivera suite of on-demand applications on an integrated development anddeployment platform. In at least one embodiment, SaaS platform maymanage and control underlying software and infrastructure for providingSaaS services. In at least one embodiment, by utilizing servicesprovided by a SaaS platform, customers can utilize applicationsexecuting on a third party network infrastructure system. In at leastone embodiment, customers can acquire an application services without aneed for customers to purchase separate licenses and support. In atleast one embodiment, various different SaaS services may be provided.In at least one embodiment, this may include, without limitation,services that provide solutions for sales performance management,enterprise integration, and business flexibility for largeorganizations.

In at least one embodiment, platform services may be provided by thirdparty network infrastructure system 1102 via a PaaS platform. In atleast one embodiment, PaaS platform may be configured to provide thirdparty network services that fall under a PaaS category. In at least oneembodiment, platform services may include without limitation servicesthat enable organizations to consolidate existing applications on ashared, common architecture, as well as an ability to build newapplications that leverage shared services provided by a platform. In atleast one embodiment, PaaS platform may manage and control underlyingsoftware and infrastructure for providing PaaS services. In at least oneembodiment, customers can acquire PaaS services provided by third partynetwork infrastructure system 1102 without a need for customers topurchase separate licenses and support.

In at least one embodiment, by utilizing services provided by a PaaSplatform, customers can employ programming languages and tools supportedby a third party network infrastructure system and also control deployedservices. In at least one embodiment, platform services provided by athird party network infrastructure system may include database thirdparty network services, middleware third party network services andthird party network services. In at least one embodiment, database thirdparty network services may support shared service deployment models thatenable organizations to pool database resources and offer customers aDatabase as a Service in a form of a database third party network. In atleast one embodiment, middleware third party network services mayprovide a platform for customers to develop and deploy various businessapplications, and third party network services may provide a platformfor customers to deploy applications, in a third party networkinfrastructure system.

In at least one embodiment, various different infrastructure servicesmay be provided by an IaaS platform in a third party networkinfrastructure system. In at least one embodiment, infrastructureservices facilitate management and control of underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by a SaaS platformand a PaaS platform.

In at least one embodiment, third party network infrastructure system1102 may also include infrastructure resources 1130 for providingresources used to provide various services to customers of a third partynetwork infrastructure system. In at least one embodiment,infrastructure resources 1130 may include pre-integrated and optimizedcombinations of hardware, such as servers, storage, and networkingresources to execute services provided by a Paas platform and a Saasplatform, and other resources.

In at least one embodiment, resources in third party networkinfrastructure system 1102 may be shared by multiple users anddynamically re-allocated per demand. In at least one embodiment,resources may be allocated to users in different time zones. In at leastone embodiment, third party network infrastructure system 1102 mayenable a first set of users in a first time zone to utilize resources ofa third party network infrastructure system for a specified number ofhours and then enable a re-allocation of same resources to another setof users located in a different time zone, thereby maximizingutilization of resources.

In at least one embodiment, a number of internal shared services 1132may be provided that are shared by different components or modules ofthird party network infrastructure system 1102 to enable provision ofservices by third party network infrastructure system 1102. In at leastone embodiment, these internal shared services may include, withoutlimitation, a security and identity service, an integration service, anenterprise repository service, an enterprise manager service, a virusscanning and white list service, a high availability, backup andrecovery service, service for enabling third party network support, anemail service, a notification service, a file transfer service, and/orvariations thereof.

In at least one embodiment, third party network infrastructure system1102 may provide comprehensive management of third party networkservices (e.g., SaaS, PaaS, and IaaS services) in a third party networkinfrastructure system. In at least one embodiment, third party networkmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by third partynetwork infrastructure system 1102, and/or variations thereof.

In at least one embodiment, as depicted in FIG. 11, third party networkmanagement functionality may be provided by one or more modules, such asan order management module 1120, an order orchestration module 1122, anorder provisioning module 1124, an order management and monitoringmodule 1126, and an identity management module 1128. In at least oneembodiment, these modules may include or be provided using one or morecomputers and/or servers, which may be general purpose computers,specialized server computers, server farms, server clusters, or anyother appropriate arrangement and/or combination.

In at least one embodiment, at step 1134, a customer using a clientdevice, such as client computing devices 1104, 1106 or 1108, mayinteract with third party network infrastructure system 1102 byrequesting one or more services provided by third party networkinfrastructure system 1102 and placing an order for a subscription forone or more services offered by third party network infrastructuresystem 1102. In at least one embodiment, a customer may access a thirdparty network User Interface (UI) such as third party network UI 1112,third party network UI 1114 and/or third party network UI 1116 and placea subscription order via these UIs. In at least one embodiment, orderinformation received by third party network infrastructure system 1102in response to a customer placing an order may include informationidentifying a customer and one or more services offered by a third partynetwork infrastructure system 1102 that a customer intends to subscribeto.

In at least one embodiment, at step 1136, an order information receivedfrom a customer may be stored in an order database 1118. In at least oneembodiment, if this is a new order, a new record may be created for anorder. In at least one embodiment, order database 1118 can be one ofseveral databases operated by third party network infrastructure system1118 and operated in conjunction with other system elements.

In at least one embodiment, at step 1138, an order information may beforwarded to an order management module 1120 that may be configured toperform billing and accounting functions related to an order, such asverifying an order, and upon verification, booking an order.

In at least one embodiment, at step 1140, information regarding an ordermay be communicated to an order orchestration module 1122 that isconfigured to orchestrate provisioning of services and resources for anorder placed by a customer. In at least one embodiment, orderorchestration module 1122 may use services of order provisioning module1124 for provisioning. In at least one embodiment, order orchestrationmodule 1122 enables management of business processes associated witheach order and applies business logic to determine whether an ordershould proceed to provisioning.

In at least one embodiment, at step 1142, upon receiving an order for anew subscription, order orchestration module 1122 sends a request toorder provisioning module 1124 to allocate resources and configureresources needed to fulfill a subscription order. In at least oneembodiment, order provisioning module 1124 enables an allocation ofresources for services ordered by a customer. In at least oneembodiment, order provisioning module 1124 provides a level ofabstraction between third party network services provided by third partynetwork infrastructure system 1100 and a physical implementation layerthat is used to provision resources for providing requested services. Inat least one embodiment, this enables order orchestration module 1122 tobe isolated from implementation details, such as whether or not servicesand resources are actually provisioned in real-time or pre-provisionedand only allocated/assigned upon request.

In at least one embodiment, at step 1144, once services and resourcesare provisioned, a notification may be sent to subscribing customersindicating that a requested service is now ready for use. In at leastone embodiment, information (e.g. a link) may be sent to a customer thatenables a customer to start using requested services.

In at least one embodiment, at step 1146, a customer's subscriptionorder may be managed and tracked by an order management and monitoringmodule 1126. In at least one embodiment, order management and monitoringmodule 1126 may be configured to collect usage statistics regarding acustomer use of subscribed services. In at least one embodiment,statistics may be collected for an amount of storage used, an amountdata transferred, a number of users, and an amount of system up time andsystem down time, and/or variations thereof.

In at least one embodiment, third party network infrastructure system1100 may include an identity management module 1128 that is configuredto provide identity services, such as access management andauthorization services in third party network infrastructure system1100. In at least one embodiment, identity management module 1128 maycontrol information about customers who wish to utilize servicesprovided by third party network infrastructure system 1102. In at leastone embodiment, such information can include information thatauthenticates identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.). In at leastone embodiment, identity management module 1128 may also includemanagement of descriptive information about each customer and about howand by whom that descriptive information can be accessed and modified.

FIG. 12 illustrates a cloud computing environment 1202, in accordancewith at least one embodiment. In at least one embodiment, cloudcomputing environment 1202 comprises one or more computer system/servers1204 with which computing devices such as, personal digital assistant(PDA) or cellular telephone 1206A, desktop computer 1206B, laptopcomputer 1206C, and/or automobile computer system 1206N communicate. Inat least one embodiment, this allows for infrastructure, platformsand/or software to be offered as services from cloud computingenvironment 1202, so as to not require each client to separatelymaintain such resources. It is understood that types of computingdevices 1206A-N shown in FIG. 12 are intended to be illustrative onlyand that cloud computing environment 1202 can communicate with any typeof computerized device over any type of network and/ornetwork/addressable connection (e.g., using a web browser).

In at least one embodiment, a computer system/server 1204, which can bedenoted as a cloud computing node, is operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. In at least one embodiment, computing systems,environments, and/or configurations that may be suitable for use withcomputer system/server 1204 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and/or variations thereof.

In at least one embodiment, computer system/server 1204 may be describedin a general context of computer system-executable instructions, such asprogram modules, being executed by a computer system. In at least oneembodiment, program modules include routines, programs, objects,components, logic, data structures, and so on, that perform particulartasks or implement particular abstract data types. In at least oneembodiment, exemplary computer system/server 1204 may be practiced indistributed loud computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. In at least one embodiment, in a distributed cloud computingenvironment, program modules may be located in both local and remotecomputer system storage media including memory storage devices.

FIG. 13 illustrates a set of functional abstraction layers provided bycloud computing environment 1202 (FIG. 12), in accordance with at leastone embodiment. It should be understood in advance that components,layers, and functions shown in FIG. 13 are intended to be illustrativeonly, and components, layers, and functions may vary.

In at least one embodiment, hardware and software layer 1302 includeshardware and software components. In at least one embodiment, hardwarecomponents include mainframes, various RISC (Reduced Instruction SetComputer) architecture based servers, various computing systems,supercomputing systems, storage devices, networks, networkingcomponents, and/or variations thereof. In at least one embodiment,software components include network application server software, variousapplication server software, various database software, and/orvariations thereof.

In at least one embodiment, virtualization layer 1304 provides anabstraction layer from which following exemplary virtual entities may beprovided: virtual servers, virtual storage, virtual networks, includingvirtual private networks, virtual applications, virtual clients, and/orvariations thereof.

In at least one embodiment, management layer 1306 provides variousfunctions. In at least one embodiment, resource provisioning providesdynamic procurement of computing resources and other resources that areutilized to perform tasks within a cloud computing environment. In atleast one embodiment, metering provides usage tracking as resources areutilized within a cloud computing environment, and billing or invoicingfor consumption of these resources. In at least one embodiment,resources may comprise application software licenses. In at least oneembodiment, security provides identity verification for users and tasks,as well as protection for data and other resources. In at least oneembodiment, user interface provides access to a cloud computingenvironment for both users and system administrators. In at least oneembodiment, service level management provides cloud computing resourceallocation and management such that required service levels are met. Inat least one embodiment, Service Level Agreement (SLA) managementprovides pre-arrangement for, and procurement of, cloud computingresources for which a future requirement is anticipated in accordancewith an SLA.

In at least one embodiment, workloads layer 1308 provides functionalityfor which a cloud computing environment is utilized. In at least oneembodiment, workloads and functions which may be provided from thislayer include: mapping and navigation, software development andmanagement, educational services, data analytics and processing,transaction processing, and service delivery.

Supercomputing

The following figures set forth, without limitation, exemplarysupercomputer-based systems that can be used to implement at least oneembodiment.

In at least one embodiment, a supercomputer may refer to a hardwaresystem exhibiting substantial parallelism and comprising at least onechip, where chips in a system are interconnected by a network and areplaced in hierarchically organized enclosures. In at least oneembodiment, a large hardware system filling a machine room, with severalracks, each containing several boards/rack modules, each containingseveral chips, all interconnected by a scalable network, is at least oneembodiment of a supercomputer. In at least one embodiment, a single rackof such a large hardware system is at least one other embodiment of asupercomputer. In at least one embodiment, a single chip exhibitingsubstantial parallelism and containing several hardware components canequally be considered to be a supercomputer, since as feature sizes maydecrease, an amount of hardware that can be incorporated in a singlechip may also increase.

FIG. 14 illustrates a supercomputer at a chip level, in accordance withat least one embodiment. In at least one embodiment, inside an FPGA orASIC chip, main computation is performed within finite state machines(1404) called thread units. In at least one embodiment, task andsynchronization networks (1402) connect finite state machines and areused to dispatch threads and execute operations in correct order. In atleast one embodiment, a multi-level partitioned on-chip cache hierarchy(1408, 1412) is accessed using memory networks (1406, 1410). In at leastone embodiment, off-chip memory is accessed using memory controllers(1416) and an off-chip memory network (1414). In at least oneembodiment, I/O controller (1418) is used for cross-chip communicationwhen a design does not fit in a single logic chip.

FIG. 15 illustrates a supercomputer at a rock module level, inaccordance with at least one embodiment. In at least one embodiment,within a rack module, there are multiple FPGA or ASIC chips (1502) thatare connected to one or more DRAM units (1504) which constitute mainaccelerator memory. In at least one embodiment, each FPGA/ASIC chip isconnected to its neighbor FPGA/ASIC chip using wide busses on a board,with differential high speed signaling (1506). In at least oneembodiment, each FPGA/ASIC chip is also connected to at least onehigh-speed serial communication cable.

FIG. 16 illustrates a supercomputer at a rack level, in accordance withat least one embodiment. FIG. 17 illustrates a supercomputer at a wholesystem level, in accordance with at least one embodiment. In at leastone embodiment, referring to FIG. 16 and FIG. 17, between rack modulesin a rack and across racks throughout an entire system, high-speedserial optical or copper cables (1602, 1702) are used to realize ascalable, possibly incomplete hypercube network. In at least oneembodiment, one of FPGA/ASIC chips of an accelerator is connected to ahost system through a PCI-Express connection (1704). In at least oneembodiment, host system comprises a host microprocessor (1708) that asoftware part of an application runs on and a memory consisting of oneor more host memory DRAM units (1706) that is kept coherent with memoryon an accelerator. In at least one embodiment, host system can be aseparate module on one of racks, or can be integrated with one of asupercomputer's modules. In at least one embodiment, cube-connectedcycles topology provide communication links to create a hypercubenetwork for a large supercomputer. In at least one embodiment, a smallgroup of FPGA/ASIC chips on a rack module can act as a single hypercubenode, such that a total number of external links of each group isincreased, compared to a single chip. In at least one embodiment, agroup contains chips A, B, C and D on a rack module with internal widedifferential busses connecting A, B, C and D in a torus organization. Inat least one embodiment, there are 12 serial communication cablesconnecting a rack module to an outside world. In at least oneembodiment, chip A on a rack module connects to serial communicationcables 0, 1, 2. In at least one embodiment, chip B connects to cables 3,4, 5. In at least one embodiment, chip C connects to 6, 7, 8. In atleast one embodiment, chip D connects to 9, 10, 11. In at least oneembodiment, an entire group {A, B, C, D} constituting a rack module canform a hypercube node within a supercomputer system, with up to 212=4096rack modules (16384 FPGA/ASIC chips). In at least one embodiment, forchip A to send a message out on link 4 of group {A, B, C, D}, a messagehas to be routed first to chip B with an on-board differential wide busconnection. In at least one embodiment, a message arriving into a group{A, B, C, D} on link 4 (i.e., arriving at B) destined to chip A, alsohas to be routed first to a correct destination chip (A) internallywithin a group {A, B, C, D}. In at least one embodiment, parallelsupercomputer systems of other sizes may also be implemented.

Artificial Intelligence

The following figures set forth, without limitation, exemplaryartificial intelligence-based systems that can be used to implement atleast one embodiment.

FIG. 18A illustrates inference and/or training logic 1815 used toperform inferencing and/or training operations associated with one ormore embodiments. Details regarding inference and/or training logic 1815are provided below in conjunction with FIGS. 18A and/or 18B.

In at least one embodiment, inference and/or training logic 1815 mayinclude, without limitation, code and/or data storage 1801 to storeforward and/or output weight and/or input/output data, and/or otherparameters to configure neurons or layers of a neural network trainedand/or used for inferencing in aspects of one or more embodiments. In atleast one embodiment, training logic 1815 may include, or be coupled tocode and/or data storage 1801 to store graph code or other software tocontrol timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs). In at least one embodiment, code, such as graph code, loadsweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which such code corresponds. In atleast one embodiment code and/or data storage 1801 stores weightparameters and/or input/output data of each layer of a neural networktrained or used in conjunction with one or more embodiments duringforward propagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, any portion of code and/or data storage 1801may be included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 1801may be internal or external to one or more processors or other hardwarelogic devices or circuits. In at least one embodiment, code and/or codeand/or data storage 1801 may be cache memory, dynamic randomlyaddressable memory (“DRAM”), static randomly addressable memory(“SRAM”), non-volatile memory (e.g., flash memory), or other storage. Inat least one embodiment, a choice of whether code and/or code and/ordata storage 1801 is internal or external to a processor, in at leastone embodiment, or comprising DRAM, SRAM, flash or some other storagetype may depend on available storage on-chip versus off-chip, latencyrequirements of training and/or inferencing functions being performed,batch size of data used in inferencing and/or training of a neuralnetwork, or some combination of these factors.

In at least one embodiment, inference and/or training logic 1815 mayinclude, without limitation, a code and/or data storage 1805 to storebackward and/or output weight and/or input/output data corresponding toneurons or layers of a neural network trained and/or used forinferencing in aspects of one or more embodiments. In at least oneembodiment, code and/or data storage 1805 stores weight parametersand/or input/output data of each layer of a neural network trained orused in conjunction with one or more embodiments during backwardpropagation of input/output data and/or weight parameters duringtraining and/or inferencing using aspects of one or more embodiments. Inat least one embodiment, training logic 1815 may include, or be coupledto code and/or data storage 1805 to store graph code or other softwareto control timing and/or order, in which weight and/or other parameterinformation is to be loaded to configure, logic, including integerand/or floating point units (collectively, arithmetic logic units(ALUs).

In at least one embodiment, code, such as graph code, causes loading ofweight or other parameter information into processor ALUs based on anarchitecture of a neural network to which such code corresponds. In atleast one embodiment, any portion of code and/or data storage 1805 maybe included with other on-chip or off-chip data storage, including aprocessor's L1, L2, or L3 cache or system memory. In at least oneembodiment, any portion of code and/or data storage 1805 may be internalor external to one or more processors or other hardware logic devices orcircuits. In at least one embodiment, code and/or data storage 1805 maybe cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory),or other storage. In at least one embodiment, a choice of whether codeand/or data storage 1805 is internal or external to a processor, in atleast one embodiment, or comprising DRAM, SRAM, flash memory or someother storage type may depend on available storage on-chip versusoff-chip, latency requirements of training and/or inferencing functionsbeing performed, batch size of data used in inferencing and/or trainingof a neural network, or some combination of these factors.

In at least one embodiment, code and/or data storage 1801 and codeand/or data storage 1805 may be separate storage structures. In at leastone embodiment, code and/or data storage 1801 and code and/or datastorage 1805 may be a combined storage structure. In at least oneembodiment, code and/or data storage 1801 and code and/or data storage1805 may be partially combined and partially separate. In at least oneembodiment, any portion of code and/or data storage 1801 and code and/ordata storage 1805 may be included with other on-chip or off-chip datastorage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, inference and/or training logic 1815 mayinclude, without limitation, one or more arithmetic logic unit(s)(“ALU(s)”) 1810, including integer and/or floating point units, toperform logical and/or mathematical operations based, at least in parton, or indicated by, training and/or inference code (e.g., graph code),a result of which may produce activations (e.g., output values fromlayers or neurons within a neural network) stored in an activationstorage 1820 that are functions of input/output and/or weight parameterdata stored in code and/or data storage 1801 and/or code and/or datastorage 1805. In at least one embodiment, activations stored inactivation storage 1820 are generated according to linear algebraic andor matrix-based mathematics performed by ALU(s) 1810 in response toperforming instructions or other code, wherein weight values stored incode and/or data storage 1805 and/or data storage 1801 are used asoperands along with other values, such as bias values, gradientinformation, momentum values, or other parameters or hyperparameters,any or all of which may be stored in code and/or data storage 1805 orcode and/or data storage 1801 or another storage on or off-chip.

In at least one embodiment, ALU(s) 1810 are included within one or moreprocessors or other hardware logic devices or circuits, whereas inanother embodiment, ALU(s) 1810 may be external to a processor or otherhardware logic device or circuit that uses them (e.g., a co-processor).In at least one embodiment, ALUs 1810 may be included within aprocessor's execution units or otherwise within a bank of ALUsaccessible by a processor's execution units either within same processoror distributed between different processors of different types (e.g.,central processing units, graphics processing units, fixed functionunits, etc.). In at least one embodiment, code and/or data storage 1801,code and/or data storage 1805, and activation storage 1820 may share aprocessor or other hardware logic device or circuit, whereas in anotherembodiment, they may be in different processors or other hardware logicdevices or circuits, or some combination of same and differentprocessors or other hardware logic devices or circuits. In at least oneembodiment, any portion of activation storage 1820 may be included withother on-chip or off-chip data storage, including a processor's L1, L2,or L3 cache or system memory. Furthermore, inferencing and/or trainingcode may be stored with other code accessible to a processor or otherhardware logic or circuit and fetched and/or processed using aprocessor's fetch, decode, scheduling, execution, retirement and/orother logical circuits.

In at least one embodiment, activation storage 1820 may be cache memory,DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage.In at least one embodiment, activation storage 1820 may be completely orpartially within or external to one or more processors or other logicalcircuits. In at least one embodiment, a choice of whether activationstorage 1820 is internal or external to a processor, in at least oneembodiment, or comprising DRAM, SRAM, flash memory or some other storagetype may depend on available storage on-chip versus off-chip, latencyrequirements of training and/or inferencing functions being performed,batch size of data used in inferencing and/or training of a neuralnetwork, or some combination of these factors.

In at least one embodiment, inference and/or training logic 1815illustrated in FIG. 18A may be used in conjunction with anapplication-specific integrated circuit (“ASIC”), such as a TensorFlow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 1815illustrated in FIG. 18A may be used in conjunction with centralprocessing unit (“CPU”) hardware, graphics processing unit (“GPU”)hardware or other hardware, such as field programmable gate arrays(“FPGAs”).

FIG. 18B illustrates inference and/or training logic 1815, according toat least one embodiment. In at least one embodiment, inference and/ortraining logic 1815 may include, without limitation, hardware logic inwhich computational resources are dedicated or otherwise exclusivelyused in conjunction with weight values or other informationcorresponding to one or more layers of neurons within a neural network.In at least one embodiment, inference and/or training logic 1815illustrated in FIG. 18B may be used in conjunction with anapplication-specific integrated circuit (ASIC), such as TensorFlow®Processing Unit from Google, an inference processing unit (IPU) fromGraphcore™, or a Nervana® (e.g., “Lake Crest”) processor from IntelCorp. In at least one embodiment, inference and/or training logic 1815illustrated in FIG. 18B may be used in conjunction with centralprocessing unit (CPU) hardware, graphics processing unit (GPU) hardwareor other hardware, such as field programmable gate arrays (FPGAs). In atleast one embodiment, inference and/or training logic 1815 includes,without limitation, code and/or data storage 1801 and code and/or datastorage 1805, which may be used to store code (e.g., graph code), weightvalues and/or other information, including bias values, gradientinformation, momentum values, and/or other parameter or hyperparameterinformation. In at least one embodiment illustrated in FIG. 18B, each ofcode and/or data storage 1801 and code and/or data storage 1805 isassociated with a dedicated computational resource, such ascomputational hardware 1802 and computational hardware 1806,respectively. In at least one embodiment, each of computational hardware1802 and computational hardware 1806 comprises one or more ALUs thatperform mathematical functions, such as linear algebraic functions, onlyon information stored in code and/or data storage 1801 and code and/ordata storage 1805, respectively, result of which is stored in activationstorage 1820.

In at least one embodiment, each of code and/or data storage 1801 and1805 and corresponding computational hardware 1802 and 1806,respectively, correspond to different layers of a neural network, suchthat resulting activation from one storage/computational pair 1801/1802of code and/or data storage 1801 and computational hardware 1802 isprovided as an input to a next storage/computational pair 1805/1806 ofcode and/or data storage 1805 and computational hardware 1806, in orderto mirror a conceptual organization of a neural network. In at least oneembodiment, each of storage/computational pairs 1801/1802 and 1805/1806may correspond to more than one neural network layer. In at least oneembodiment, additional storage/computation pairs (not shown) subsequentto or in parallel with storage/computation pairs 1801/1802 and 1805/1806may be included in inference and/or training logic 1815.

FIG. 19 illustrates training and deployment of a deep neural network,according to at least one embodiment. In at least one embodiment,untrained neural network 1906 is trained using a training dataset 1902.In at least one embodiment, training framework 1904 is a PyTorchframework, whereas in other embodiments, training framework 1904 is aTensorFlow, Boost, Caffe, Microsoft Cognitive Toolkit/CNTK, MXNet,Chainer, Keras, Deeplearning4j, or other training framework. In at leastone embodiment, training framework 1904 trains an untrained neuralnetwork 1906 and enables it to be trained using processing resourcesdescribed herein to generate a trained neural network 1908. In at leastone embodiment, weights may be chosen randomly or by pre-training usinga deep belief network. In at least one embodiment, training may beperformed in either a supervised, partially supervised, or unsupervisedmanner.

In at least one embodiment, untrained neural network 1906 is trainedusing supervised learning, wherein training dataset 1902 includes aninput paired with a desired output for an input, or where trainingdataset 1902 includes input having a known output and an output ofneural network 1906 is manually graded. In at least one embodiment,untrained neural network 1906 is trained in a supervised manner andprocesses inputs from training dataset 1902 and compares resultingoutputs against a set of expected or desired outputs. In at least oneembodiment, errors are then propagated back through untrained neuralnetwork 1906. In at least one embodiment, training framework 1904adjusts weights that control untrained neural network 1906. In at leastone embodiment, training framework 1904 includes tools to monitor howwell untrained neural network 1906 is converging towards a model, suchas trained neural network 1908, suitable to generating correct answers,such as in result 1914, based on input data such as a new dataset 1912.In at least one embodiment, training framework 1904 trains untrainedneural network 1906 repeatedly while adjust weights to refine an outputof untrained neural network 1906 using a loss function and adjustmentalgorithm, such as stochastic gradient descent. In at least oneembodiment, training framework 1904 trains untrained neural network 1906until untrained neural network 1906 achieves a desired accuracy. In atleast one embodiment, trained neural network 1908 can then be deployedto implement any number of machine learning operations.

In at least one embodiment, untrained neural network 1906 is trainedusing unsupervised learning, wherein untrained neural network 1906attempts to train itself using unlabeled data. In at least oneembodiment, unsupervised learning training dataset 1902 will includeinput data without any associated output data or “ground truth” data. Inat least one embodiment, untrained neural network 1906 can learngroupings within training dataset 1902 and can determine how individualinputs are related to untrained dataset 1902. In at least oneembodiment, unsupervised training can be used to generate aself-organizing map in trained neural network 1908 capable of performingoperations useful in reducing dimensionality of new dataset 1912. In atleast one embodiment, unsupervised training can also be used to performanomaly detection, which allows identification of data points in newdataset 1912 that deviate from normal patterns of new dataset 1912.

In at least one embodiment, semi-supervised learning may be used, whichis a technique in which in training dataset 1902 includes a mix oflabeled and unlabeled data. In at least one embodiment, trainingframework 1904 may be used to perform incremental learning, such asthrough transferred learning techniques. In at least one embodiment,incremental learning enables trained neural network 1908 to adapt to newdataset 1912 without forgetting knowledge instilled within trainedneural network 1408 during initial training.

5G Networks

The following figures set forth, without limitation, exemplary 5Gnetwork-based systems that can be used to implement at least oneembodiment.

FIG. 20 illustrates architecture of a system 2000 of a network, inaccordance with at least one embodiment. In at least one embodiment,system 2000 is shown to include a user equipment (UE) 2002 and a UE2004. In at least one embodiment, UEs 2002 and 2004 are illustrated assmartphones (e.g., handheld touchscreen mobile computing devicesconnectable to one or more cellular networks) but may also comprise anymobile or non-mobile computing device, such as Personal Data Assistants(PDAs), pagers, laptop computers, desktop computers, wireless handsets,or any computing device including a wireless communications interface.

In at least one embodiment, any of UEs 2002 and 2004 can comprise anInternet of Things (IoT) UE, which can comprise a network access layerdesigned for low-power IoT applications utilizing short-lived UEconnections. In at least one embodiment, an IoT UE can utilizetechnologies such as machine-to-machine (M2M) or machine-typecommunications (MTC) for exchanging data with an MTC server or devicevia a public land mobile network (PLMN), Proximity-Based Service (ProSe)or device-to-device (D2D) communication, sensor networks, or IoTnetworks. In at least one embodiment, a M2M or MTC exchange of data maybe a machine-initiated exchange of data. In at least one embodiment, anIoT network describes interconnecting IoT UEs, which may includeuniquely identifiable embedded computing devices (within Internetinfrastructure), with short-lived connections. In at least oneembodiment, an IoT UEs may execute background applications (e.g., keepalive messages, status updates, etc.) to facilitate connections of anIoT network.

In at least one embodiment, UEs 2002 and 2004 may be configured toconnect, e.g., communicatively couple, with a radio access network (RAN)2016. In at least one embodiment, RAN 2016 may be, in at least oneembodiment, an Evolved Universal Mobile Telecommunications System (UMTS)Terrestrial Radio Access Network (E-UTRAN), a NextGen RAN (NG RAN), orsome other type of RAN. In at least one embodiment, UEs 2002 and 2004utilize connections 2012 and 2014, respectively, each of which comprisesa physical communications interface or layer. In at least oneembodiment, connections 2012 and 2014 are illustrated as an airinterface to enable communicative coupling, and can be consistent withcellular communications protocols, such as a Global System for MobileCommunications (GSM) protocol, a code-division multiple access (CDMA)network protocol, a Push-to-Talk (PTT) protocol, a PTT over Cellular(POC) protocol, a Universal Mobile Telecommunications System (UMTS)protocol, a 3GPP Long Term Evolution (LTE) protocol, a fifth generation(5G) protocol, a New Radio (NR) protocol, and variations thereof.

In at least one embodiment, UEs 2002 and 2004 may further directlyexchange communication data via a ProSe interface 2006. In at least oneembodiment, ProSe interface 2006 may alternatively be referred to as asidelink interface comprising one or more logical channels, includingbut not limited to a Physical Sidelink Control Channel (PSCCH), aPhysical Sidelink Shared Channel (PSSCH), a Physical Sidelink DiscoveryChannel (PSDCH), and a Physical Sidelink Broadcast Channel (PSBCH).

In at least one embodiment, UE 2004 is shown to be configured to accessan access point (AP) 2010 via connection 2008. In at least oneembodiment, connection 2008 can comprise a local wireless connection,such as a connection consistent with any IEEE 802.11 protocol, whereinAP 2010 would comprise a wireless fidelity (WiFi®) router. In at leastone embodiment, AP 2010 is shown to be connected to an Internet withoutconnecting to a core network of a wireless system.

In at least one embodiment, RAN 2016 can include one or more accessnodes that enable connections 2012 and 2014. In at least one embodiment,these access nodes (ANs) can be referred to as base stations (BSs),NodeBs, evolved NodeBs (eNBs), next Generation NodeBs (gNB), RAN nodes,and so forth, and can comprise ground stations (e.g., terrestrial accesspoints) or satellite stations providing coverage within a geographicarea (e.g., a cell). In at least one embodiment, RAN 2016 may includeone or more RAN nodes for providing macrocells, e.g., macro RAN node2018, and one or more RAN nodes for providing femtocells or picocells(e.g., cells having smaller coverage areas, smaller user capacity, orhigher bandwidth compared to macrocells), e.g., low power (LP) RAN node2020.

In at least one embodiment, any of RAN nodes 2018 and 2020 can terminatean air interface protocol and can be a first point of contact for UEs2002 and 2004. In at least one embodiment, any of RAN nodes 2018 and2020 can fulfill various logical functions for RAN 2016 including, butnot limited to, radio network controller (RNC) functions such as radiobearer management, uplink and downlink dynamic radio resource managementand data packet scheduling, and mobility management.

In at least one embodiment, UEs 2002 and 2004 can be configured tocommunicate using Orthogonal Frequency-Division Multiplexing (OFDM)communication signals with each other or with any of RAN nodes 2018 and2020 over a multi-carrier communication channel in accordance variouscommunication techniques, such as, but not limited to, an OrthogonalFrequency Division Multiple Access (OFDMA) communication technique(e.g., for downlink communications) or a Single Carrier FrequencyDivision Multiple Access (SC-FDMA) communication technique (e.g., foruplink and ProSe or sidelink communications), and/or variations thereof.In at least one embodiment, OFDM signals can comprise a plurality oforthogonal sub-carriers.

In at least one embodiment, a downlink resource grid can be used fordownlink transmissions from any of RAN nodes 2018 and 2020 to UEs 2002and 2004, while uplink transmissions can utilize similar techniques. Inat least one embodiment, a grid can be a time frequency grid, called aresource grid or time-frequency resource grid, which is a physicalresource in a downlink in each slot. In at least one embodiment, such atime frequency plane representation is a common practice for OFDMsystems, which makes it intuitive for radio resource allocation. In atleast one embodiment, each column and each row of a resource gridcorresponds to one OFDM symbol and one OFDM subcarrier, respectively. Inat least one embodiment, a duration of a resource grid in a time domaincorresponds to one slot in a radio frame. In at least one embodiment, asmallest time-frequency unit in a resource grid is denoted as a resourceelement. In at least one embodiment, each resource grid comprises anumber of resource blocks, which describe a mapping of certain physicalchannels to resource elements. In at least one embodiment, each resourceblock comprises a collection of resource elements. In at least oneembodiment, in a frequency domain, this may represent a smallestquantity of resources that currently can be allocated. In at least oneembodiment, there are several different physical downlink channels thatare conveyed using such resource blocks.

In at least one embodiment, a physical downlink shared channel (PDSCH)may carry user data and higher-layer signaling to UEs 2002 and 2004. Inat least one embodiment, a physical downlink control channel (PDCCH) maycarry information about a transport format and resource allocationsrelated to PDSCH channel, among other things. In at least oneembodiment, it may also inform UEs 2002 and 2004 about a transportformat, resource allocation, and HARQ (Hybrid Automatic Repeat Request)information related to an uplink shared channel. In at least oneembodiment, typically, downlink scheduling (assigning control and sharedchannel resource blocks to UE 2002 within a cell) may be performed atany of RAN nodes 2018 and 2020 based on channel quality information fedback from any of UEs 2002 and 2004. In at least one embodiment, downlinkresource assignment information may be sent on a PDCCH used for (e.g.,assigned to) each of UEs 2002 and 2004.

In at least one embodiment, a PDCCH may use control channel elements(CCEs) to convey control information. In at least one embodiment, beforebeing mapped to resource elements, PDCCH complex valued symbols mayfirst be organized into quadruplets, which may then be permuted using asub-block interleaver for rate matching. In at least one embodiment,each PDCCH may be transmitted using one or more of these CCEs, whereeach CCE may correspond to nine sets of four physical resource elementsknown as resource element groups (REGs). In at least one embodiment,four Quadrature Phase Shift Keying (QPSK) symbols may be mapped to eachREG. In at least one embodiment, PDCCH can be transmitted using one ormore CCEs, depending on a size of a downlink control information (DCI)and a channel condition. In at least one embodiment, there can be fouror more different PDCCH formats defined in LTE with different numbers ofCCEs (e.g., aggregation level, L=1, 2, 4, or 8).

In at least one embodiment, an enhanced physical downlink controlchannel (EPDCCH) that uses PDSCH resources may be utilized for controlinformation transmission. In at least one embodiment, EPDCCH may betransmitted using one or more enhanced control channel elements (ECCEs).In at least one embodiment, each ECCE may correspond to nine sets offour physical resource elements known as an enhanced resource elementgroups (EREGs). In at least one embodiment, an ECCE may have othernumbers of EREGs in some situations.

In at least one embodiment, RAN 2016 is shown to be communicativelycoupled to a core network (CN) 2038 via an S1 interface 2022. In atleast one embodiment, CN 2038 may be an evolved packet core (EPC)network, a NextGen Packet Core (NPC) network, or some other type of CN.In at least one embodiment, S1 interface 2022 is split into two parts:S1-U interface 2026, which carries traffic data between RAN nodes 2018and 2020 and serving gateway (S-GW) 2030, and a S1-mobility managemententity (MME) interface 2024, which is a signaling interface between RANnodes 2018 and 2020 and MMEs 2028.

In at least one embodiment, CN 2038 comprises MMEs 2028, S-GW 2030,Packet Data Network (PDN) Gateway (P-GW) 2034, and a home subscriberserver (HSS) 2032. In at least one embodiment, MMEs 2028 may be similarin function to a control plane of legacy Serving General Packet RadioService (GPRS) Support Nodes (SGSN). In at least one embodiment, MMEs2028 may manage mobility aspects in access such as gateway selection andtracking area list management. In at least one embodiment, HSS 2032 maycomprise a database for network users, including subscription relatedinformation to support a network entities' handling of communicationsessions. In at least one embodiment, CN 2038 may comprise one orseveral HSSs 2032, depending on a number of mobile subscribers, on acapacity of an equipment, on an organization of a network, etc. In atleast one embodiment, HSS 2032 can provide support for routing/roaming,authentication, authorization, naming/addressing resolution, locationdependencies, etc.

In at least one embodiment, S-GW 2030 may terminate a S1 interface 2022towards RAN 2016, and routes data packets between RAN 2016 and CN 2038.In at least one embodiment, S-GW 2030 may be a local mobility anchorpoint for inter-RAN node handovers and also may provide an anchor forinter-3GPP mobility. In at least one embodiment, other responsibilitiesmay include lawful intercept, charging, and some policy enforcement.

In at least one embodiment, P-GW 2034 may terminate an SGi interfacetoward a PDN. In at least one embodiment, P-GW 2034 may route datapackets between an EPC network 2038 and external networks such as anetwork including application server 2040 (alternatively referred to asapplication function (AF)) via an Internet Protocol (IP) interface 2042.In at least one embodiment, application server 2040 may be an elementoffering applications that use IP bearer resources with a core network(e.g., UMTS Packet Services (PS) domain, LTE PS data services, etc.). Inat least one embodiment, P-GW 2034 is shown to be communicativelycoupled to an application server 2040 via an IP communications interface2042. In at least one embodiment, application server 2040 can also beconfigured to support one or more communication services (e.g.,Voice-over-Internet Protocol (VoIP) sessions, PTT sessions, groupcommunication sessions, social networking services, etc.) for UEs 2002and 2004 via CN 2038.

In at least one embodiment, P-GW 2034 may further be a node for policyenforcement and charging data collection. In at least one embodiment,policy and Charging Enforcement Function (PCRF) 2036 is a policy andcharging control element of CN 2038. In at least one embodiment, in anon-roaming scenario, there may be a single PCRF in a Home Public LandMobile Network (HPLMN) associated with a UE's Internet ProtocolConnectivity Access Network (IP-CAN) session. In at least oneembodiment, in a roaming scenario with local breakout of traffic, theremay be two PCRFs associated with a UE's IP-CAN session: a Home PCRF(H-PCRF) within a HPLMN and a Visited PCRF (V-PCRF) within a VisitedPublic Land Mobile Network (VPLMN). In at least one embodiment, PCRF2036 may be communicatively coupled to application server 2040 via P-GW2034. In at least one embodiment, application server 2040 may signalPCRF 2036 to indicate a new service flow and select an appropriateQuality of Service (QoS) and charging parameters. In at least oneembodiment, PCRF 2036 may provision this rule into a Policy and ChargingEnforcement Function (PCEF) (not shown) with an appropriate traffic flowtemplate (TFT) and QoS class of identifier (QCI), which commences a QoSand charging as specified by application server 2040.

FIG. 21 illustrates an architecture of a system 2100 of a network inaccordance with some embodiments. In at least one embodiment, system2100 is shown to include a UE 2102, a 5G access node or RAN node (shownas (R)AN node 2108), a User Plane Function (shown as UPF 2104), a DataNetwork (DN 2106), which may be, in at least one embodiment, operatorservices, Internet access or 3rd party services, and a 5G Core Network(5GC) (shown as CN 2110).

In at least one embodiment, CN 2110 includes an Authentication ServerFunction (AUSF 2114); a Core Access and Mobility Management Function(AMF 2112); a Session Management Function (SMF 2118); a Network ExposureFunction (NEF 2116); a Policy Control Function (PCF 2122); a NetworkFunction (NF) Repository Function (NRF 2120); a Unified Data Management(UDM 2124); and an Application Function (AF 2126). In at least oneembodiment, CN 2110 may also include other elements that are not shown,such as a Structured Data Storage network function (SDSF), anUnstructured Data Storage network function (UDSF), and variationsthereof.

In at least one embodiment, UPF 2104 may act as an anchor point forintra-RAT and inter-RAT mobility, an external PDU session point ofinterconnect to DN 2106, and a branching point to support multi-homedPDU session. In at least one embodiment, UPF 2104 may also performpacket routing and forwarding, packet inspection, enforce user planepart of policy rules, lawfully intercept packets (UP collection);traffic usage reporting, perform QoS handling for user plane (e.g.packet filtering, gating, UL/DL rate enforcement), perform UplinkTraffic verification (e.g., SDF to QoS flow mapping), transport levelpacket marking in uplink and downlink, and downlink packet buffering anddownlink data notification triggering. In at least one embodiment, UPF2104 may include an uplink classifier to support routing traffic flowsto a data network. In at least one embodiment, DN 2106 may representvarious network operator services, Internet access, or third partyservices.

In at least one embodiment, AUSF 2114 may store data for authenticationof UE 2102 and handle authentication related functionality. In at leastone embodiment, AUSF 2114 may facilitate a common authenticationframework for various access types.

In at least one embodiment, AMF 2112 may be responsible for registrationmanagement (e.g., for registering UE 2102, etc.), connection management,reachability management, mobility management, and lawful interception ofAMF-related events, and access authentication and authorization. In atleast one embodiment, AMF 2112 may provide transport for SM messages forSMF 2118, and act as a transparent proxy for routing SM messages. In atleast one embodiment, AMF 2112 may also provide transport for shortmessage service (SMS) messages between UE 2102 and an SMS function(SMSF) (not shown by FIG. 21). In at least one embodiment, AMF 2112 mayact as Security Anchor Function (SEA), which may include interactionwith AUSF 2114 and UE 2102 and receipt of an intermediate key that wasestablished as a result of UE 2102 authentication process. In at leastone embodiment, where USIM based authentication is used, AMF 2112 mayretrieve security material from AUSF 2114. In at least one embodiment,AMF 2112 may also include a Security Context Management (SCM) function,which receives a key from SEA that it uses to derive access-networkspecific keys. In at least one embodiment, furthermore, AMF 2112 may bea termination point of RAN CP interface (N2 reference point), atermination point of NAS (NI) signaling, and perform NAS ciphering andintegrity protection.

In at least one embodiment, AMF 2112 may also support NAS signaling witha UE 2102 over an N3 interworking-function (IWF) interface. In at leastone embodiment, N3IWF may be used to provide access to untrustedentities. In at least one embodiment, N3IWF may be a termination pointfor N2 and N3 interfaces for control plane and user plane, respectively,and as such, may handle N2 signaling from SMF and AMF for PDU sessionsand QoS, encapsulate/de-encapsulate packets for IPSec and N3 tunneling,mark N3 user-plane packets in uplink, and enforce QoS corresponding toN3 packet marking taking into account QoS requirements associated tosuch marking received over N2. In at least one embodiment, N3IWF mayalso relay uplink and downlink control-plane NAS (NI) signaling betweenUE 2102 and AMF 2112, and relay uplink and downlink user-plane packetsbetween UE 2102 and UPF 2104. In at least one embodiment, N3IWF alsoprovides mechanisms for IPsec tunnel establishment with UE 2102.

In at least one embodiment, SMF 2118 may be responsible for sessionmanagement (e.g., session establishment, modify and release, includingtunnel maintain between UPF and AN node); UE IP address allocation &management (including optional Authorization); Selection and control ofUP function; Configures traffic steering at UPF to route traffic toproper destination; termination of interfaces towards Policy controlfunctions; control part of policy enforcement and QoS; lawful intercept(for SM events and interface to LI System); termination of SM parts ofNAS messages; downlink Data Notification; initiator of AN specific SMinformation, sent via AMF over N2 to AN; determine SSC mode of asession. In at least one embodiment, SMF 2118 may include followingroaming functionality: handle local enforcement to apply QoS SLAB(VPLMN); charging data collection and charging interface (VPLMN); lawfulintercept (in VPLMN for SM events and interface to LI System); supportfor interaction with external DN for transport of signaling for PDUsession authorization/authentication by external DN.

In at least one embodiment, NEF 2116 may provide means for securelyexposing services and capabilities provided by 3GPP network functionsfor third party, internal exposure/re-exposure, Application Functions(e.g., AF 2126), edge computing or fog computing systems, etc. In atleast one embodiment, NEF 2116 may authenticate, authorize, and/orthrottle AFs. In at least one embodiment, NEF 2116 may also translateinformation exchanged with AF 2126 and information exchanged withinternal network functions. In at least one embodiment, NEF 2116 maytranslate between an AF-Service-Identifier and an internal 5GCinformation. In at least one embodiment, NEF 2116 may also receiveinformation from other network functions (NFs) based on exposedcapabilities of other network functions. In at least one embodiment,this information may be stored at NEF 2116 as structured data, or at adata storage NF using a standardized interfaces. In at least oneembodiment, stored information can then be re-exposed by NEF 2116 toother NFs and AFs, and/or used for other purposes such as analytics.

In at least one embodiment, NRF 2120 may support service discoveryfunctions, receive NF Discovery Requests from NF instances, and provideinformation of discovered NF instances to NF instances. In at least oneembodiment, NRF 2120 also maintains information of available NFinstances and their supported services.

In at least one embodiment, PCF 2122 may provide policy rules to controlplane function(s) to enforce them, and may also support unified policyframework to govern network behavior. In at least one embodiment, PCF2122 may also implement a front end (FE) to access subscriptioninformation relevant for policy decisions in a UDR of UDM 2124.

In at least one embodiment, UDM 2124 may handle subscription-relatedinformation to support a network entities' handling of communicationsessions, and may store subscription data of UE 2102. In at least oneembodiment, UDM 2124 may include two parts, an application FE and a UserData Repository (UDR). In at least one embodiment, UDM may include a UDMFE, which is in charge of processing of credentials, locationmanagement, subscription management and so on. In at least oneembodiment, several different front ends may serve a same user indifferent transactions. In at least one embodiment, UDM-FE accessessubscription information stored in an UDR and performs authenticationcredential processing; user identification handling; accessauthorization; registration/mobility management; and subscriptionmanagement. In at least one embodiment, UDR may interact with PCF 2122.In at least one embodiment, UDM 2124 may also support SMS management,wherein an SMS-FE implements a similar application logic as discussedpreviously.

In at least one embodiment, AF 2126 may provide application influence ontraffic routing, access to a Network Capability Exposure (NCE), andinteract with a policy framework for policy control. In at least oneembodiment, NCE may be a mechanism that allows a 5GC and AF 2126 toprovide information to each other via NEF 2116, which may be used foredge computing implementations. In at least one embodiment, networkoperator and third party services may be hosted close to UE 2102 accesspoint of attachment to achieve an efficient service delivery through areduced end-to-end latency and load on a transport network. In at leastone embodiment, for edge computing implementations, 5GC may select a UPF2104 close to UE 2102 and execute traffic steering from UPF 2104 to DN2106 via N6 interface. In at least one embodiment, this may be based onUE subscription data, UE location, and information provided by AF 2126.In at least one embodiment, AF 2126 may influence UPF (re)selection andtraffic routing. In at least one embodiment, based on operatordeployment, when AF 2126 is considered to be a trusted entity, a networkoperator may permit AF 2126 to interact directly with relevant NFs.

In at least one embodiment, CN 2110 may include an SMSF, which may beresponsible for SMS subscription checking and verification, and relayingSM messages to/from UE 2102 to/from other entities, such as anSMS-GMSC/IWMSC/SMS-router. In at least one embodiment, SMS may alsointeract with AMF 2112 and UDM 2124 for notification procedure that UE2102 is available for SMS transfer (e.g., set a UE not reachable flag,and notifying UDM 2124 when UE 2102 is available for SMS).

In at least one embodiment, system 2100 may include followingservice-based interfaces: Namf: Service-based interface exhibited byAMF; Nsmf: Service-based interface exhibited by SMF; Nnef: Service-basedinterface exhibited by NEF; Npcf: Service-based interface exhibited byPCF; Nudm: Service-based interface exhibited by UDM; Naf: Service-basedinterface exhibited by AF; Nnrf: Service-based interface exhibited byNRF; and Nausf: Service-based interface exhibited by AUSF.

In at least one embodiment, system 2100 may include following referencepoints: N1: Reference point between UE and AMF; N2: Reference pointbetween (R)AN and AMF; N3: Reference point between (R)AN and UPF; N4:Reference point between SMF and UPF; and N6: Reference point between UPFand a Data Network. In at least one embodiment, there may be many morereference points and/or service-based interfaces between a NF servicesin NFs, however, these interfaces and reference points have been omittedfor clarity. In at least one embodiment, an NS reference point may bebetween a PCF and AF; an N7 reference point may be between PCF and SMF;an N11 reference point between AMF and SMF; etc. In at least oneembodiment, CN 2110 may include an Nx interface, which is an inter-CNinterface between MME and AMF 2112 in order to enable interworkingbetween CN 2110 and CN 7221.

In at least one embodiment, system 2100 may include multiple RAN nodes(such as (R)AN node 2108) wherein an Xn interface is defined between twoor more (R)AN node 2108 (e.g., gNBs) that connecting to 5GC 410, betweena (R)AN node 2108 (e.g., gNB) connecting to CN 2110 and an eNB (e.g., amacro RAN node), and/or between two eNBs connecting to CN 2110.

In at least one embodiment, Xn interface may include an Xn user plane(Xn-U) interface and an Xn control plane (Xn-C) interface. In at leastone embodiment, Xn-U may provide non-guaranteed delivery of user planePDUs and support/provide data forwarding and flow control functionality.In at least one embodiment, Xn-C may provide management and errorhandling functionality, functionality to manage a Xn-C interface;mobility support for UE 2102 in a connected mode (e.g., CM-CONNECTED)including functionality to manage UE mobility for connected mode betweenone or more (R)AN node 2108. In at least one embodiment, mobilitysupport may include context transfer from an old (source) serving (R)ANnode 2108 to new (target) serving (R)AN node 2108; and control of userplane tunnels between old (source) serving (R)AN node 2108 to new(target) serving (R)AN node 2108.

In at least one embodiment, a protocol stack of a Xn-U may include atransport network layer built on Internet Protocol (IP) transport layer,and a GTP-U layer on top of a UDP and/or IP layer(s) to carry user planePDUs. In at least one embodiment, Xn-C protocol stack may include anapplication layer signaling protocol (referred to as Xn ApplicationProtocol (Xn-AP)) and a transport network layer that is built on an SCTPlayer. In at least one embodiment, SCTP layer may be on top of an IPlayer. In at least one embodiment, SCTP layer provides a guaranteeddelivery of application layer messages. In at least one embodiment, in atransport IP layer point-to-point transmission is used to deliversignaling PDUs. In at least one embodiment, Xn-U protocol stack and/or aXn-C protocol stack may be same or similar to an user plane and/orcontrol plane protocol stack(s) shown and described herein.

FIG. 22 is an illustration of a control plane protocol stack inaccordance with some embodiments. In at least one embodiment, a controlplane 2200 is shown as a communications protocol stack between UE 2002(or alternatively, UE 2004), RAN 2016, and MME(s) 2028.

In at least one embodiment, PHY layer 2202 may transmit or receiveinformation used by MAC layer 2204 over one or more air interfaces. Inat least one embodiment, PHY layer 2202 may further perform linkadaptation or adaptive modulation and coding (AMC), power control, cellsearch (e.g., for initial synchronization and handover purposes), andother measurements used by higher layers, such as an RRC layer 2210. Inat least one embodiment, PHY layer 2202 may still further perform errordetection on transport channels, forward error correction (FEC)coding/de-coding of transport channels, modulation/demodulation ofphysical channels, interleaving, rate matching, mapping onto physicalchannels, and Multiple Input Multiple Output (MIMO) antenna processing.

In at least one embodiment, MAC layer 2204 may perform mapping betweenlogical channels and transport channels, multiplexing of MAC servicedata units (SDUs) from one or more logical channels onto transportblocks (TB) to be delivered to PHY via transport channels,de-multiplexing MAC SDUs to one or more logical channels from transportblocks (TB) delivered from PHY via transport channels, multiplexing MACSDUs onto TBs, scheduling information reporting, error correctionthrough hybrid automatic repeat request (HARD), and logical channelprioritization.

In at least one embodiment, RLC layer 2206 may operate in a plurality ofmodes of operation, including: Transparent Mode (TM), UnacknowledgedMode (UM), and Acknowledged Mode (AM). In at least one embodiment, RLClayer 2206 may execute transfer of upper layer protocol data units(PDUs), error correction through automatic repeat request (ARQ) for AMdata transfers, and concatenation, segmentation and reassembly of RLCSDUs for UM and AM data transfers. In at least one embodiment, RLC layer2206 may also execute re-segmentation of RLC data PDUs for AM datatransfers, reorder RLC data PDUs for UM and AM data transfers, detectduplicate data for UM and AM data transfers, discard RLC SDUs for UM andAM data transfers, detect protocol errors for AM data transfers, andperform RLC re-establishment.

In at least one embodiment, PDCP layer 2208 may execute headercompression and decompression of IP data, maintain PDCP Sequence Numbers(SNs), perform in-sequence delivery of upper layer PDUs atre-establishment of lower layers, eliminate duplicates of lower layerSDUs at re-establishment of lower layers for radio bearers mapped on RLCAM, cipher and decipher control plane data, perform integrity protectionand integrity verification of control plane data, control timer-baseddiscard of data, and perform security operations (e.g., ciphering,deciphering, integrity protection, integrity verification, etc.).

In at least one embodiment, main services and functions of a RRC layer2210 may include broadcast of system information (e.g., included inMaster Information Blocks (MIBs) or System Information Blocks (SIBs)related to a non-access stratum (NAS)), broadcast of system informationrelated to an access stratum (AS), paging, establishment, maintenanceand release of an RRC connection between an UE and E-UTRAN (e.g., RRCconnection paging, RRC connection establishment, RRC connectionmodification, and RRC connection release), establishment, configuration,maintenance and release of point-to-point radio bearers, securityfunctions including key management, inter radio access technology (RAT)mobility, and measurement configuration for UE measurement reporting. Inat least one embodiment, said MIBs and SIBs may comprise one or moreinformation elements (IEs), which may each comprise individual datafields or data structures.

In at least one embodiment, UE 2002 and RAN 2016 may utilize a Uuinterface (e.g., an LTE-Uu interface) to exchange control plane data viaa protocol stack comprising PHY layer 2202, MAC layer 2204, RLC layer2206, PDCP layer 2208, and RRC layer 2210.

In at least one embodiment, non-access stratum (NAS) protocols (NASprotocols 2212) form a highest stratum of a control plane between UE2002 and MME(s) 2028. In at least one embodiment, NAS protocols 2212support mobility of UE 2002 and session management procedures toestablish and maintain IP connectivity between UE 2002 and P-GW 2034.

In at least one embodiment, Si Application Protocol (S1-AP) layer (Si-APlayer 2222) may support functions of a Si interface and compriseElementary Procedures (EPs). In at least one embodiment, an EP is a unitof interaction between RAN 2016 and CN 2028. In at least one embodiment,S1-AP layer services may comprise two groups: UE-associated services andnon UE-associated services. In at least one embodiment, these servicesperform functions including, but not limited to: E-UTRAN Radio AccessBearer (E-RAB) management, UE capability indication, mobility, NASsignaling transport, RAN Information Management (RIM), and configurationtransfer.

In at least one embodiment, Stream Control Transmission Protocol (SCTP)layer (alternatively referred to as a stream control transmissionprotocol/internet protocol (SCTP/IP) layer) (SCTP layer 2220) may ensurereliable delivery of signaling messages between RAN 2016 and MME(s) 2028based, in part, on an IP protocol, supported by an IP layer 2218. In atleast one embodiment, L2 layer 2216 and an L1 layer 2214 may refer tocommunication links (e.g., wired or wireless) used by a RAN node and MMEto exchange information.

In at least one embodiment, RAN 2016 and MME(s) 2028 may utilize anS1-MME interface to exchange control plane data via a protocol stackcomprising a L1 layer 2214, L2 layer 2216, IP layer 2218, SCTP layer2220, and Si-AP layer 2222.

FIG. 23 is an illustration of a user plane protocol stack in accordancewith at least one embodiment. In at least one embodiment, a user plane2300 is shown as a communications protocol stack between a UE 2002, RAN2016, S-GW 2030, and P-GW 2034. In at least one embodiment, user plane2300 may utilize a same protocol layers as control plane 2200. In atleast one embodiment, UE 2002 and RAN 2016 may utilize a Uu interface(e.g., an LTE-Uu interface) to exchange user plane data via a protocolstack comprising PHY layer 2202, MAC layer 2204, RLC layer 2206, PDCPlayer 2208.

In at least one embodiment, General Packet Radio Service (GPRS)Tunneling Protocol for a user plane (GTP-U) layer (GTP-U layer 2304) maybe used for carrying user data within a GPRS core network and between aradio access network and a core network. In at least one embodiment,user data transported can be packets in any of IPv4, IPv6, or PPPformats. In at least one embodiment, UDP and IP security (UDP/IP) layer(UDP/IP layer 2302) may provide checksums for data integrity, portnumbers for addressing different functions at a source and destination,and encryption and authentication on selected data flows. In at leastone embodiment, RAN 2016 and S-GW 2030 may utilize an S1-U interface toexchange user plane data via a protocol stack comprising L1 layer 2214,L2 layer 2216, UDP/IP layer 2302, and GTP-U layer 2304. In at least oneembodiment, S-GW 2030 and P-GW 2034 may utilize an S5/S8a interface toexchange user plane data via a protocol stack comprising L1 layer 2214,L2 layer 2216, UDP/IP layer 2302, and GTP-U layer 2304. In at least oneembodiment, as discussed above with respect to FIG. 22, NAS protocolssupport a mobility of UE 2002 and session management procedures toestablish and maintain IP connectivity between UE 2002 and P-GW 2034.

FIG. 24 illustrates components 2400 of a core network in accordance withat least one embodiment. In at least one embodiment, components of CN2038 may be implemented in one physical node or separate physical nodesincluding components to read and execute instructions from amachine-readable or computer-readable medium (e.g., a non-transitorymachine-readable storage medium). In at least one embodiment, NetworkFunctions Virtualization (NFV) is utilized to virtualize any or all ofabove described network node functions via executable instructionsstored in one or more computer readable storage mediums (described infurther detail below). In at least one embodiment, a logicalinstantiation of CN 2038 may be referred to as a network slice 2402(e.g., network slice 2402 is shown to include HSS 2032, MME(s) 2028, andS-GW 2030). In at least one embodiment, a logical instantiation of aportion of CN 2038 may be referred to as a network sub-slice 2404 (e.g.,network sub-slice 2404 is shown to include P-GW 2034 and PCRF 2036).

In at least one embodiment, NFV architectures and infrastructures may beused to virtualize one or more network functions, alternativelyperformed by proprietary hardware, onto physical resources comprising acombination of industry-standard server hardware, storage hardware, orswitches. In at least one embodiment, NFV systems can be used to executevirtual or reconfigurable implementations of one or more EPCcomponents/functions.

FIG. 25 is a block diagram illustrating components, according to atleast one embodiment, of a system 2500 to support network functionvirtualization (NFV). In at least one embodiment, system 2500 isillustrated as including a virtualized infrastructure manager (shown asVIM 2502), a network function virtualization infrastructure (shown asNFVI 2504), a VNF manager (shown as VNFM 2506), virtualized networkfunctions (shown as VNF 2508), an element manager (shown as EM 2510), anNFV Orchestrator (shown as NFVO 2512), and a network manager (shown asNM 2514).

In at least one embodiment, VIM 2502 manages resources of NFVI 2504. Inat least one embodiment, NFVI 2504 can include physical or virtualresources and applications (including hypervisors) used to executesystem 2500. In at least one embodiment, VIM 2502 may manage a lifecycle of virtual resources with NFVI 2504 (e.g., creation, maintenance,and tear down of virtual machines (VMs) associated with one or morephysical resources), track VM instances, track performance, fault andsecurity of VM instances and associated physical resources, and exposeVM instances and associated physical resources to other managementsystems.

In at least one embodiment, VNFM 2506 may manage VNF 2508. In at leastone embodiment, VNF 2508 may be used to execute EPCcomponents/functions. In at least one embodiment, VNFM 2506 may manage alife cycle of VNF 2508 and track performance, fault and security ofvirtual aspects of VNF 2508. In at least one embodiment, EM 2510 maytrack performance, fault and security of functional aspects of VNF 2508.In at least one embodiment, tracking data from VNFM 2506 and EM 2510 maycomprise, in at least one embodiment, performance measurement (PM) dataused by VIM 2502 or NFVI 2504. In at least one embodiment, both VNFM2506 and EM 2510 can scale up/down a quantity of VNFs of system 2500.

In at least one embodiment, NFVO 2512 may coordinate, authorize, releaseand engage resources of NFVI 2504 in order to provide a requestedservice (e.g., to execute an EPC function, component, or slice). In atleast one embodiment, NM 2514 may provide a package of end-userfunctions with responsibility for a management of a network, which mayinclude network elements with VNFs, non-virtualized network functions,or both (management of VNFs may occur via an EM 2510).

Computer-Based Systems

The following figures set forth, without limitation, exemplarycomputer-based systems that can be used to implement at least oneembodiment.

FIG. 26 illustrates a processing system 2600, in accordance with atleast one embodiment. In at least one embodiment, processing system 2600includes one or more processors 2602 and one or more graphics processors2608, and may be a single processor desktop system, a multiprocessorworkstation system, or a server system having a large number ofprocessors 2602 or processor cores 2607. In at least one embodiment,processing system 2600 is a processing platform incorporated within asystem-on-a-chip (“SoC”) integrated circuit for use in mobile, handheld,or embedded devices.

In at least one embodiment, processing system 2600 can include, or beincorporated within a server-based gaming platform, a game console, amedia console, a mobile gaming console, a handheld game console, or anonline game console. In at least one embodiment, processing system 2600is a mobile phone, smart phone, tablet computing device or mobileInternet device. In at least one embodiment, processing system 2600 canalso include, couple with, or be integrated within a wearable device,such as a smart watch wearable device, smart eyewear device, augmentedreality device, or virtual reality device. In at least one embodiment,processing system 2600 is a television or set top box device having oneor more processors 2602 and a graphical interface generated by one ormore graphics processors 2608.

In at least one embodiment, one or more processors 2602 each include oneor more processor cores 2607 to process instructions which, whenexecuted, perform operations for system and user software. In at leastone embodiment, each of one or more processor cores 2607 is configuredto process a specific instruction set 2609. In at least one embodiment,instruction set 2609 may facilitate Complex Instruction Set Computing(“CISC”), Reduced Instruction Set Computing (“RISC”), or computing via aVery Long Instruction Word (“VLIW”). In at least one embodiment,processor cores 2607 may each process a different instruction set 2609,which may include instructions to facilitate emulation of otherinstruction sets. In at least one embodiment, processor core 2607 mayalso include other processing devices, such as a digital signalprocessor (“DSP”).

In at least one embodiment, processor 2602 includes cache memory(‘cache”) 2604. In at least one embodiment, processor 2602 can have asingle internal cache or multiple levels of internal cache. In at leastone embodiment, cache memory is shared among various components ofprocessor 2602. In at least one embodiment, processor 2602 also uses anexternal cache (e.g., a Level 3 (“L3”) cache or Last Level Cache(“LLC”)) (not shown), which may be shared among processor cores 2607using known cache coherency techniques. In at least one embodiment,register file 2606 is additionally included in processor 2602 which mayinclude different types of registers for storing different types of data(e.g., integer registers, floating point registers, status registers,and an instruction pointer register). In at least one embodiment,register file 2606 may include general-purpose registers or otherregisters.

In at least one embodiment, one or more processor(s) 2602 are coupledwith one or more interface bus(es) 2610 to transmit communicationsignals such as address, data, or control signals between processor 2602and other components in processing system 2600. In at least oneembodiment interface bus 2610, in one embodiment, can be a processorbus, such as a version of a Direct Media Interface (“DMI”) bus. In atleast one embodiment, interface bus 2610 is not limited to a DMI bus,and may include one or more Peripheral Component Interconnect buses(e.g., “PCI,” PCI Express (“PCIe”)), memory buses, or other types ofinterface buses. In at least one embodiment processor(s) 2602 include anintegrated memory controller 2616 and a platform controller hub 2630. Inat least one embodiment, memory controller 2616 facilitatescommunication between a memory device and other components of processingsystem 2600, while platform controller hub (“PCH”) 2630 providesconnections to Input/Output (“I/O”) devices via a local I/O bus.

In at least one embodiment, memory device 2620 can be a dynamic randomaccess memory (“DRAM”) device, a static random access memory (“SRAM”)device, flash memory device, phase-change memory device, or some othermemory device having suitable performance to serve as processor memory.In at least one embodiment memory device 2620 can operate as systemmemory for processing system 2600, to store data 2622 and instructions2621 for use when one or more processors 2602 executes an application orprocess. In at least one embodiment, memory controller 2616 also coupleswith an optional external graphics processor 2612, which may communicatewith one or more graphics processors 2608 in processors 2602 to performgraphics and media operations. In at least one embodiment, a displaydevice 2611 can connect to processor(s) 2602. In at least one embodimentdisplay device 2611 can include one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In at least one embodiment, display device 2611 caninclude a head mounted display (“HMD”) such as a stereoscopic displaydevice for use in virtual reality (“VR”) applications or augmentedreality (“AR”) applications.

In at least one embodiment, platform controller hub 2630 enablesperipherals to connect to memory device 2620 and processor 2602 via ahigh-speed I/O bus. In at least one embodiment, I/O peripherals include,but are not limited to, an audio controller 2646, a network controller2634, a firmware interface 2628, a wireless transceiver 2626, touchsensors 2625, a data storage device 2624 (e.g., hard disk drive, flashmemory, etc.). In at least one embodiment, data storage device 2624 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as PCI, or PCIe. In at least one embodiment, touch sensors 2625 caninclude touch screen sensors, pressure sensors, or fingerprint sensors.In at least one embodiment, wireless transceiver 2626 can be a Wi-Fitransceiver, a Bluetooth transceiver, or a mobile network transceiversuch as a 3G, 4G, or Long Term Evolution (“LTE”) transceiver. In atleast one embodiment, firmware interface 2628 enables communication withsystem firmware, and can be, in at least one embodiment, a unifiedextensible firmware interface (“UEFI”). In at least one embodiment,network controller 2634 can enable a network connection to a wirednetwork. In at least one embodiment, a high-performance networkcontroller (not shown) couples with interface bus 2610. In at least oneembodiment, audio controller 2646 is a multi-channel high definitionaudio controller. In at least one embodiment, processing system 2600includes an optional legacy I/O controller 2640 for coupling legacy(e.g., Personal System 2 (“PS/2”)) devices to processing system 2600. Inat least one embodiment, platform controller hub 2630 can also connectto one or more Universal Serial Bus (“USB”) controllers 2642 connectinput devices, such as keyboard and mouse 2643 combinations, a camera2644, or other USB input devices.

In at least one embodiment, an instance of memory controller 2616 andplatform controller hub 2630 may be integrated into a discreet externalgraphics processor, such as external graphics processor 2612. In atleast one embodiment, platform controller hub 2630 and/or memorycontroller 2616 may be external to one or more processor(s) 2602. In atleast one embodiment, processing system 2600 can include an externalmemory controller 2616 and platform controller hub 2630, which may beconfigured as a memory controller hub and peripheral controller hubwithin a system chipset that is in communication with processor(s) 2602.

FIG. 27 illustrates a computer system 2700, in accordance with at leastone embodiment. In at least one embodiment, computer system 2700 may bea system with interconnected devices and components, an SOC, or somecombination. In at least on embodiment, computer system 2700 is formedwith a processor 2702 that may include execution units to execute aninstruction. In at least one embodiment, computer system 2700 mayinclude, without limitation, a component, such as processor 2702 toemploy execution units including logic to perform algorithms forprocessing data. In at least one embodiment, computer system 2700 mayinclude processors, such as PENTIUM® Processor family, Xeon™, Itanium®,XScale™ and/or StrongARM™, Intel® Core™, or Intel® Nervana™microprocessors available from Intel Corporation of Santa Clara, Calif.,although other systems (including PCs having other microprocessors,engineering workstations, set-top boxes and like) may also be used. Inat least one embodiment, computer system 2700 may execute a version ofWINDOWS' operating system available from Microsoft Corporation ofRedmond, Wash., although other operating systems (UNIX and Linux in atleast one embodiment), embedded software, and/or graphical userinterfaces, may also be used.

In at least one embodiment, computer system 2700 may be used in otherdevices such as handheld devices and embedded applications. Some ones ofthe at least one embodiments of handheld devices include cellularphones, Internet Protocol devices, digital cameras, personal digitalassistants (“PDAs”), and handheld PCs. In at least one embodiment,embedded applications may include a microcontroller, a digital signalprocessor (DSP), an SoC, network computers (“NetPCs”), set-top boxes,network hubs, wide area network (“WAN”) switches, or any other systemthat may perform one or more instructions.

In at least one embodiment, computer system 2700 may include, withoutlimitation, processor 2702 that may include, without limitation, one ormore execution units 2708 that may be configured to execute a ComputeUnified Device Architecture (“CUDA”) (CUDA® is developed by NVIDIACorporation of Santa Clara, Calif.) program. In at least one embodiment,a CUDA program is at least a portion of a software application writtenin a CUDA programming language. In at least one embodiment, computersystem 2700 is a single processor desktop or server system. In at leastone embodiment, computer system 2700 may be a multiprocessor system. Inat least one embodiment, processor 2702 may include, without limitation,a CISC microprocessor, a RISC microprocessor, a VLIW microprocessor, aprocessor implementing a combination of instruction sets, or any otherprocessor device, such as a digital signal processor, in at least oneembodiment. In at least one embodiment, processor 2702 may be coupled toa processor bus 2710 that may transmit data signals between processor2702 and other components in computer system 2700.

In at least one embodiment, processor 2702 may include, withoutlimitation, a Level 1 (“L1”) internal cache memory (“cache”) 2704. In atleast one embodiment, processor 2702 may have a single internal cache ormultiple levels of internal cache. In at least one embodiment, cachememory may reside external to processor 2702. In at least oneembodiment, processor 2702 may also include a combination of bothinternal and external caches. In at least one embodiment, a registerfile 2706 may store different types of data in various registersincluding, without limitation, integer registers, floating pointregisters, status registers, and instruction pointer register.

In at least one embodiment, execution unit 2708, including, withoutlimitation, logic to perform integer and floating point operations, alsoresides in processor 2702. Processor 2702 may also include a microcode(“ucode”) read only memory (“ROM”) that stores microcode for certainmacro instructions. In at least one embodiment, execution unit 2708 mayinclude logic to handle a packed instruction set 2709. In at least oneembodiment, by including packed instruction set 2709 in an instructionset of a general-purpose processor 2702, along with associated circuitryto execute instructions, operations used by many multimedia applicationsmay be performed using packed data in a general-purpose processor 2702.In at least one embodiment, many multimedia applications may beaccelerated and executed more efficiently by using full width of aprocessor's data bus for performing operations on packed data, which mayeliminate a need to transfer smaller units of data across a processor'sdata bus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit 2708 may also be used inmicrocontrollers, embedded processors, graphics devices, DSPs, and othertypes of logic circuits. In at least one embodiment, computer system2700 may include, without limitation, a memory 2720. In at least oneembodiment, memory 2720 may be implemented as a DRAM device, an SRAMdevice, flash memory device, or other memory device. Memory 2720 maystore instruction(s) 2719 and/or data 2721 represented by data signalsthat may be executed by processor 2702.

In at least one embodiment, a system logic chip may be coupled toprocessor bus 2710 and memory 2720. In at least one embodiment, a systemlogic chip may include, without limitation, a memory controller hub(“MCH”) 2716, and processor 2702 may communicate with MCH 2716 viaprocessor bus 2710. In at least one embodiment, MCH 2716 may provide ahigh bandwidth memory path 2718 to memory 2720 for instruction and datastorage and for storage of graphics commands, data and textures. In atleast one embodiment, MCH 2716 may direct data signals between processor2702, memory 2720, and other components in computer system 2700 and tobridge data signals between processor bus 2710, memory 2720, and asystem I/O 2722. In at least one embodiment, system logic chip mayprovide a graphics port for coupling to a graphics controller. In atleast one embodiment, MCH 2716 may be coupled to memory 2720 throughhigh bandwidth memory path 2718 and graphics/video card 2712 may becoupled to MCH 2716 through an Accelerated Graphics Port (“AGP”)interconnect 2714.

In at least one embodiment, computer system 2700 may use system I/O 2722that is a proprietary hub interface bus to couple MCH 2716 to I/Ocontroller hub (“ICH”) 2730. In at least one embodiment, ICH 2730 mayprovide direct connections to some I/O devices via a local I/O bus. Inat least one embodiment, local I/O bus may include, without limitation,a high-speed I/O bus for connecting peripherals to memory 2720, achipset, and processor 2702. Examples may include, without limitation,an audio controller 2729, a firmware hub (“flash BIOS”) 2728, a wirelesstransceiver 2726, a data storage 2724, a legacy I/O controller 2723containing a user input interface 2725 and a keyboard interface, aserial expansion port 2777, such as a USB, and a network controller2734. Data storage 2724 may comprise a hard disk drive, a floppy diskdrive, a CD-ROM device, a flash memory device, or other mass storagedevice.

In at least one embodiment, FIG. 27 illustrates a system, which includesinterconnected hardware devices or “chips.” In at least one embodiment,FIG. 27 may illustrate an exemplary SoC. In at least one embodiment,devices illustrated in FIG. 27 may be interconnected with proprietaryinterconnects, standardized interconnects (e.g., PCIe), or somecombination thereof. In at least one embodiment, one or more componentsof system 2700 are interconnected using compute express link (“CXL”)interconnects.

FIG. 28 illustrates a system 2800, in accordance with at least oneembodiment. In at least one embodiment, system 2800 is an electronicdevice that utilizes a processor 2810. In at least one embodiment,system 2800 may be, in at least one embodiment and without limitation, anotebook, a tower server, a rack server, a blade server, a laptop, adesktop, a tablet, a mobile device, a phone, an embedded computer, orany other suitable electronic device.

In at least one embodiment, system 2800 may include, without limitation,processor 2810 communicatively coupled to any suitable number or kind ofcomponents, peripherals, modules, or devices. In at least oneembodiment, processor 2810 is coupled using a bus or interface, such asan I2C bus, a System Management Bus (“SMBus”), a Low Pin Count (“LPC”)bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio(“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a USB(versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter(“UART”) bus. In at least one embodiment, FIG. 28 illustrates a systemwhich includes interconnected hardware devices or “chips.” In at leastone embodiment, FIG. 28 may illustrate an exemplary SoC. In at least oneembodiment, devices illustrated in FIG. 28 may be interconnected withproprietary interconnects, standardized interconnects (e.g., PCIe) orsome combination thereof. In at least one embodiment, one or morecomponents of FIG. 28 are interconnected using CXL interconnects.

In at least one embodiment, FIG. 28 may include a display 2824, a touchscreen 2825, a touch pad 2830, a Near Field Communications unit (“NFC”)2845, a sensor hub 2840, a thermal sensor 2846, an Express Chipset(“EC”) 2835, a Trusted Platform Module (“TPM”) 2838, BIOS/firmware/flashmemory (“BIOS, FW Flash”) 2822, a DSP 2860, a Solid State Disk (“SSD”)or Hard Disk Drive (“HDD”) 2820, a wireless local area network unit(“WLAN”) 2850, a Bluetooth unit 2852, a Wireless Wide Area Network unit(“WWAN”) 2856, a Global Positioning System (“GPS”) 2855, a camera (“USB3.0 camera”) 2854 such as a USB 3.0 camera, or a Low Power Double DataRate (“LPDDR”) memory unit (“LPDDR3”) 2815 implemented, in at least oneembodiment, LPDDR3 standard. These components may each be implemented inany suitable manner.

In at least one embodiment, other components may be communicativelycoupled to processor 2810 through components discussed above. In atleast one embodiment, an accelerometer 2841, an Ambient Light Sensor(“ALS”) 2842, a compass 2843, and a gyroscope 2844 may becommunicatively coupled to sensor hub 2840. In at least one embodiment,a thermal sensor 2839, a fan 2837, a keyboard 2846, and a touch pad 2830may be communicatively coupled to EC 2835. In at least one embodiment, aspeaker 2863, a headphones 2864, and a microphone (“mic”) 2865 may becommunicatively coupled to an audio unit (“audio codec and class d amp”)2864, which may in turn be communicatively coupled to DSP 2860. In atleast one embodiment, audio unit 2864 may include, without limitation,an audio coder/decoder (“codec”) and a class D amplifier. In at leastone embodiment, a SIM card (“SIM”) 2857 may be communicatively coupledto WWAN unit 2856. In at least one embodiment, components such as WLANunit 2850 and Bluetooth unit 2852, as well as WWAN unit 2856 may beimplemented in a Next Generation Form Factor (“NGFF”).

FIG. 29 illustrates an exemplary integrated circuit 2900, in accordancewith at least one embodiment. In at least one embodiment, exemplaryintegrated circuit 2900 is an SoC that may be fabricated using one ormore IP cores. In at least one embodiment, integrated circuit 2900includes one or more application processor(s) 2905 (e.g., CPUs), atleast one graphics processor 2910, and may additionally include an imageprocessor 2915 and/or a video processor 2920, any of which may be amodular IP core. In at least one embodiment, integrated circuit 2900includes peripheral or bus logic including a USB controller 2925, a UARTcontroller 2930, an SPI/SDIO controller 2935, and an I2S/I2C controller2940. In at least one embodiment, integrated circuit 2900 can include adisplay device 2945 coupled to one or more of a high-definitionmultimedia interface (“HDMI”) controller 2950 and a mobile industryprocessor interface (“MIPI”) display interface 2955. In at least oneembodiment, storage may be provided by a flash memory subsystem 2960including flash memory and a flash memory controller. In at least oneembodiment, a memory interface may be provided via a memory controller2965 for access to SDRAM or SRAM memory devices. In at least oneembodiment, some integrated circuits additionally include an embeddedsecurity engine 2970.

FIG. 30 illustrates a computing system 3000, according to at least oneembodiment; In at least one embodiment, computing system 3000 includes aprocessing subsystem 3001 having one or more processor(s) 3002 and asystem memory 3004 communicating via an interconnection path that mayinclude a memory hub 3005. In at least one embodiment, memory hub 3005may be a separate component within a chipset component or may beintegrated within one or more processor(s) 3002. In at least oneembodiment, memory hub 3005 couples with an I/O subsystem 3011 via acommunication link 3006. In at least one embodiment, I/O subsystem 3011includes an I/O hub 3007 that can enable computing system 3000 toreceive input from one or more input device(s) 3008. In at least oneembodiment, I/O hub 3007 can enable a display controller, which may beincluded in one or more processor(s) 3002, to provide outputs to one ormore display device(s) 3010A. In at least one embodiment, one or moredisplay device(s) 3010A coupled with I/O hub 3007 can include a local,internal, or embedded display device.

In at least one embodiment, processing subsystem 3001 includes one ormore parallel processor(s) 3012 coupled to memory hub 3005 via a bus orother communication link 3013. In at least one embodiment, communicationlink 3013 may be one of any number of standards based communication linktechnologies or protocols, such as, but not limited to PCIe, or may be avendor specific communications interface or communications fabric. In atleast one embodiment, one or more parallel processor(s) 3012 form acomputationally focused parallel or vector processing system that caninclude a large number of processing cores and/or processing clusters,such as a many integrated core processor. In at least one embodiment,one or more parallel processor(s) 3012 form a graphics processingsubsystem that can output pixels to one of one or more display device(s)3010A coupled via I/O Hub 3007. In at least one embodiment, one or moreparallel processor(s) 3012 can also include a display controller anddisplay interface (not shown) to enable a direct connection to one ormore display device(s) 3010B.

In at least one embodiment, a system storage unit 3014 can connect toI/O hub 3007 to provide a storage mechanism for computing system 3000.In at least one embodiment, an I/O switch 3016 can be used to provide aninterface mechanism to enable connections between I/O hub 3007 and othercomponents, such as a network adapter 3018 and/or wireless networkadapter 3019 that may be integrated into a platform, and various otherdevices that can be added via one or more add-in device(s) 3020. In atleast one embodiment, network adapter 3018 can be an Ethernet adapter oranother wired network adapter. In at least one embodiment, wirelessnetwork adapter 3019 can include one or more of a Wi-Fi, Bluetooth, NFC,or other network device that includes one or more wireless radios.

In at least one embodiment, computing system 3000 can include othercomponents not explicitly shown, including USB or other portconnections, optical storage drives, video capture devices, and/orvariations thereof, that may also be connected to I/O hub 3007. In atleast one embodiment, communication paths interconnecting variouscomponents in FIG. 30 may be implemented using any suitable protocols,such as PCI based protocols (e.g., PCIe), or other bus or point-to-pointcommunication interfaces and/or protocol(s), such as NVLink high-speedinterconnect, or interconnect protocols.

In at least one embodiment, one or more parallel processor(s) 3012incorporate circuitry optimized for graphics and video processing,including, in at least one embodiment, video output circuitry, andconstitutes a graphics processing unit (“GPU”). In at least oneembodiment, one or more parallel processor(s) 3012 incorporate circuitryoptimized for general purpose processing. In at least embodiment,components of computing system 3000 may be integrated with one or moreother system elements on a single integrated circuit. In at least oneembodiment, one or more parallel processor(s) 3012, memory hub 3005,processor(s) 3002, and I/O hub 3007 can be integrated into a SoCintegrated circuit. In at least one embodiment, components of computingsystem 3000 can be integrated into a single package to form a system inpackage (“SIP”) configuration. In at least one embodiment, at least aportion of components of computing system 3000 can be integrated into amulti-chip module (“MCM”), which can be interconnected with othermulti-chip modules into a modular computing system. In at least oneembodiment, I/O subsystem 3011 and display devices 3010B are omittedfrom computing system 3000.

Processing Systems

The following figures set forth, without limitation, exemplaryprocessing systems that can be used to implement at least oneembodiment.

FIG. 31 illustrates an accelerated processing unit (“APU”) 3100, inaccordance with at least one embodiment. In at least one embodiment, APU3100 is developed by AMD Corporation of Santa Clara, Calif. In at leastone embodiment, APU 3100 can be configured to execute an applicationprogram, such as a CUDA program. In at least one embodiment, APU 3100includes, without limitation, a core complex 3110, a graphics complex3140, fabric 3160, I/O interfaces 3170, memory controllers 3180, adisplay controller 3192, and a multimedia engine 3194. In at least oneembodiment, APU 3100 may include, without limitation, any number of corecomplexes 3110, any number of graphics complexes 3150, any number ofdisplay controllers 3192, and any number of multimedia engines 3194 inany combination. For explanatory purposes, multiple instances of likeobjects are denoted herein with reference numbers identifying an objectand parenthetical numbers identifying an instance where needed.

In at least one embodiment, core complex 3110 is a CPU, graphics complex3140 is a GPU, and APU 3100 is a processing unit that integrates,without limitation, 3110 and 3140 onto a single chip. In at least oneembodiment, some tasks may be assigned to core complex 3110 and othertasks may be assigned to graphics complex 3140. In at least oneembodiment, core complex 3110 is configured to execute main controlsoftware associated with APU 3100, such as an operating system. In atleast one embodiment, core complex 3110 is a master processor of APU3100, controlling and coordinating operations of other processors. In atleast one embodiment, core complex 3110 issues commands that control anoperation of graphics complex 3140. In at least one embodiment, corecomplex 3110 can be configured to execute host executable code derivedfrom CUDA source code, and graphics complex 3140 can be configured toexecute device executable code derived from CUDA source code.

In at least one embodiment, core complex 3110 includes, withoutlimitation, cores 3120(1)-3120(4) and an L3 cache 3130. In at least oneembodiment, core complex 3110 may include, without limitation, anynumber of cores 3120 and any number and type of caches in anycombination. In at least one embodiment, cores 3120 are configured toexecute instructions of a particular instruction set architecture(“ISA”). In at least one embodiment, each core 3120 is a CPU core.

In at least one embodiment, each core 3120 includes, without limitation,a fetch/decode unit 3122, an integer execution engine 3124, a floatingpoint execution engine 3126, and an L2 cache 3128. In at least oneembodiment, fetch/decode unit 3122 fetches instructions, decodes suchinstructions, generates micro-operations, and dispatches separatemicro-instructions to integer execution engine 3124 and floating pointexecution engine 3126. In at least one embodiment, fetch/decode unit3122 can concurrently dispatch one micro-instruction to integerexecution engine 3124 and another micro-instruction to floating pointexecution engine 3126. In at least one embodiment, integer executionengine 3124 executes, without limitation, integer and memory operations.In at least one embodiment, floating point engine 3126 executes, withoutlimitation, floating point and vector operations. In at least oneembodiment, fetch-decode unit 3122 dispatches micro-instructions to asingle execution engine that replaces both integer execution engine 3124and floating point execution engine 3126.

In at least one embodiment, each core 3120(i), where i is an integerrepresenting a particular instance of core 3120, may access L2 cache3128(i) included in core 3120(i). In at least one embodiment, each core3120 included in core complex 3110(j), where j is an integerrepresenting a particular instance of core complex 3110, is connected toother cores 3120 included in core complex 3110(j) via L3 cache 3130(j)included in core complex 3110(j). In at least one embodiment, cores 3120included in core complex 3110(j), where j is an integer representing aparticular instance of core complex 3110, can access all of L3 cache3130(j) included in core complex 3110(j). In at least one embodiment, L3cache 3130 may include, without limitation, any number of slices.

In at least one embodiment, graphics complex 3140 can be configured toperform compute operations in a highly-parallel fashion. In at least oneembodiment, graphics complex 3140 is configured to execute graphicspipeline operations such as draw commands, pixel operations, geometriccomputations, and other operations associated with rendering an image toa display. In at least one embodiment, graphics complex 3140 isconfigured to execute operations unrelated to graphics. In at least oneembodiment, graphics complex 3140 is configured to execute bothoperations related to graphics and operations unrelated to graphics.

In at least one embodiment, graphics complex 3140 includes, withoutlimitation, any number of compute units 3150 and an L2 cache 3142. In atleast one embodiment, compute units 3150 share L2 cache 3142. In atleast one embodiment, L2 cache 3142 is partitioned. In at least oneembodiment, graphics complex 3140 includes, without limitation, anynumber of compute units 3150 and any number (including zero) and type ofcaches. In at least one embodiment, graphics complex 3140 includes,without limitation, any amount of dedicated graphics hardware.

In at least one embodiment, each compute unit 3150 includes, withoutlimitation, any number of SIMD units 3152 and a shared memory 3154. Inat least one embodiment, each SIMD unit 3152 implements a SIMDarchitecture and is configured to perform operations in parallel. In atleast one embodiment, each compute unit 3150 may execute any number ofthread blocks, but each thread block executes on a single compute unit3150. In at least one embodiment, a thread block includes, withoutlimitation, any number of threads of execution. In at least oneembodiment, a workgroup is a thread block. In at least one embodiment,each SIMD unit 3152 executes a different warp. In at least oneembodiment, a warp is a group of threads (e.g., 16 threads), where eachthread in a warp belongs to a single thread block and is configured toprocess a different set of data based on a single set of instructions.In at least one embodiment, predication can be used to disable one ormore threads in a warp. In at least one embodiment, a lane is a thread.In at least one embodiment, a work item is a thread. In at least oneembodiment, a wavefront is a warp. In at least one embodiment, differentwavefronts in a thread block may synchronize together and communicatevia shared memory 3154.

In at least one embodiment, fabric 3160 is a system interconnect thatfacilitates data and control transmissions across core complex 3110,graphics complex 3140, I/O interfaces 3170, memory controllers 3180,display controller 3192, and multimedia engine 3194. In at least oneembodiment, APU 3100 may include, without limitation, any amount andtype of system interconnect in addition to or instead of fabric 3160that facilitates data and control transmissions across any number andtype of directly or indirectly linked components that may be internal orexternal to APU 3100. In at least one embodiment, I/O interfaces 3170are representative of any number and type of I/O interfaces (e.g., PCI,PCI-Extended (“PCI-X”), PCIe, gigabit Ethernet (“GBE”), USB, etc.). Inat least one embodiment, various types of peripheral devices are coupledto I/O interfaces 3170 In at least one embodiment, peripheral devicesthat are coupled to I/O interfaces 3170 may include, without limitation,keyboards, mice, printers, scanners, joysticks or other types of gamecontrollers, media recording devices, external storage devices, networkinterface cards, and so forth.

In at least one embodiment, display controller AMD92 displays images onone or more display device(s), such as a liquid crystal display (“LCD”)device. In at least one embodiment, multimedia engine 240 includes,without limitation, any amount and type of circuitry that is related tomultimedia, such as a video decoder, a video encoder, an image signalprocessor, etc. In at least one embodiment, memory controllers 3180facilitate data transfers between APU 3100 and a unified system memory3190. In at least one embodiment, core complex 3110 and graphics complex3140 share unified system memory 3190.

In at least one embodiment, APU 3100 implements a memory subsystem thatincludes, without limitation, any amount and type of memory controllers3180 and memory devices (e.g., shared memory 3154) that may be dedicatedto one component or shared among multiple components. In at least oneembodiment, APU 3100 implements a cache subsystem that includes, withoutlimitation, one or more cache memories (e.g., L2 caches 2728, L3 cache3130, and L2 cache 3142) that may each be private to or shared betweenany number of components (e.g., cores 3120, core complex 3110, SIMDunits 3152, compute units 3150, and graphics complex 3140).

FIG. 32 illustrates a CPU 3200, in accordance with at least oneembodiment. In at least one embodiment, CPU 3200 is developed by AMDCorporation of Santa Clara, Calif. In at least one embodiment, CPU 3200can be configured to execute an application program. In at least oneembodiment, CPU 3200 is configured to execute main control software,such as an operating system. In at least one embodiment, CPU 3200 issuescommands that control an operation of an external GPU (not shown). In atleast one embodiment, CPU 3200 can be configured to execute hostexecutable code derived from CUDA source code, and an external GPU canbe configured to execute device executable code derived from such CUDAsource code. In at least one embodiment, CPU 3200 includes, withoutlimitation, any number of core complexes 3210, fabric 3260, I/Ointerfaces 3270, and memory controllers 3280.

In at least one embodiment, core complex 3210 includes, withoutlimitation, cores 3220(1)-3220(4) and an L3 cache 3230. In at least oneembodiment, core complex 3210 may include, without limitation, anynumber of cores 3220 and any number and type of caches in anycombination. In at least one embodiment, cores 3220 are configured toexecute instructions of a particular ISA. In at least one embodiment,each core 3220 is a CPU core.

In at least one embodiment, each core 3220 includes, without limitation,a fetch/decode unit 3222, an integer execution engine 3224, a floatingpoint execution engine 3226, and an L2 cache 3228. In at least oneembodiment, fetch/decode unit 3222 fetches instructions, decodes suchinstructions, generates micro-operations, and dispatches separatemicro-instructions to integer execution engine 3224 and floating pointexecution engine 3226. In at least one embodiment, fetch/decode unit3222 can concurrently dispatch one micro-instruction to integerexecution engine 3224 and another micro-instruction to floating pointexecution engine 3226. In at least one embodiment, integer executionengine 3224 executes, without limitation, integer and memory operations.In at least one embodiment, floating point engine 3226 executes, withoutlimitation, floating point and vector operations. In at least oneembodiment, fetch-decode unit 3222 dispatches micro-instructions to asingle execution engine that replaces both integer execution engine 3224and floating point execution engine 3226.

In at least one embodiment, each core 3220(i), where i is an integerrepresenting a particular instance of core 3220, may access L2 cache3228(i) included in core 3220(i). In at least one embodiment, each core3220 included in core complex 3210(j), where j is an integerrepresenting a particular instance of core complex 3210, is connected toother cores 3220 in core complex 3210(j) via L3 cache 3230(j) includedin core complex 3210(j). In at least one embodiment, cores 3220 includedin core complex 3210(j), where j is an integer representing a particularinstance of core complex 3210, can access all of L3 cache 3230(j)included in core complex 3210(j). In at least one embodiment, L3 cache3230 may include, without limitation, any number of slices.

In at least one embodiment, fabric 3260 is a system interconnect thatfacilitates data and control transmissions across core complexes3210(1)-3210(N) (where N is an integer greater than zero), I/Ointerfaces 3270, and memory controllers 3280. In at least oneembodiment, CPU 3200 may include, without limitation, any amount andtype of system interconnect in addition to or instead of fabric 3260that facilitates data and control transmissions across any number andtype of directly or indirectly linked components that may be internal orexternal to CPU 3200. In at least one embodiment, I/O interfaces 3270are representative of any number and type of I/O interfaces (e.g., PCI,PCI-X, PCIe, GBE, USB, etc.). In at least one embodiment, various typesof peripheral devices are coupled to I/O interfaces 3270 In at least oneembodiment, peripheral devices that are coupled to I/O interfaces 3270may include, without limitation, displays, keyboards, mice, printers,scanners, joysticks or other types of game controllers, media recordingdevices, external storage devices, network interface cards, and soforth.

In at least one embodiment, memory controllers 3280 facilitate datatransfers between CPU 3200 and a system memory 3290. In at least oneembodiment, core complex 3210 and graphics complex 3240 share systemmemory 3290. In at least one embodiment, CPU 3200 implements a memorysubsystem that includes, without limitation, any amount and type ofmemory controllers 3280 and memory devices that may be dedicated to onecomponent or shared among multiple components. In at least oneembodiment, CPU 3200 implements a cache subsystem that includes, withoutlimitation, one or more cache memories (e.g., L2 caches 3228 and L3caches 3230) that may each be private to or shared between any number ofcomponents (e.g., cores 3220 and core complexes 3210).

FIG. 33 illustrates an exemplary accelerator integration slice 3390, inaccordance with at least one embodiment. As used herein, a “slice”comprises a specified portion of processing resources of an acceleratorintegration circuit. In at least one embodiment, an acceleratorintegration circuit provides cache management, memory access, contextmanagement, and interrupt management services on behalf of multiplegraphics processing engines included in a graphics acceleration module.Graphics processing engines may each comprise a separate GPU.Alternatively, graphics processing engines may comprise different typesof graphics processing engines within a GPU such as graphics executionunits, media processing engines (e.g., video encoders/decoders),samplers, and blit engines. In at least one embodiment, a graphicsacceleration module may be a GPU with multiple graphics processingengines. In at least one embodiment, graphics processing engines may beindividual GPUs integrated on a common package, line card, or chip.

An application effective address space 3382 within system memory 3314stores process elements 3383. In one embodiment, process elements 3383are stored in response to GPU invocations 3381 from applications 3380executed on processor 3307. A process element 3383 contains processstate for corresponding application 3380. A work descriptor (“WD”) 3384contained in process element 3383 can be a single job requested by anapplication or may contain a pointer to a queue of jobs. In at least oneembodiment, WD 3384 is a pointer to a job request queue in applicationeffective address space 3382.

Graphics acceleration module 3346 and/or individual graphics processingengines can be shared by all or a subset of processes in a system. In atleast one embodiment, an infrastructure for setting up process state andsending WD 3384 to graphics acceleration module 3346 to start a job in avirtualized environment may be included.

In at least one embodiment, a dedicated-process programming model isimplementation-specific. In this model, a single process owns graphicsacceleration module 3346 or an individual graphics processing engine.Because graphics acceleration module 3346 is owned by a single process,a hypervisor initializes an accelerator integration circuit for anowning partition and an operating system initializes acceleratorintegration circuit for an owning process when graphics accelerationmodule 3346 is assigned.

In operation, a WD fetch unit 3391 in accelerator integration slice 3390fetches next WD 3384 which includes an indication of work to be done byone or more graphics processing engines of graphics acceleration module3346. Data from WD 3384 may be stored in registers 3345 and used by amemory management unit (“MMU”) 3339, interrupt management circuit 3347and/or context management circuit 3348 as illustrated. In at least oneembodiment of MMU 3339 includes segment/page walk circuitry foraccessing segment/page tables 3386 within OS virtual address space 3385.Interrupt management circuit 3347 may process interrupt events (“INT”)3392 received from graphics acceleration module 3346. When performinggraphics operations, an effective address 3393 generated by a graphicsprocessing engine is translated to a real address by MMU 3339.

In one embodiment, a same set of registers 3345 are duplicated for eachgraphics processing engine and/or graphics acceleration module 3346 andmay be initialized by a hypervisor or operating system. Each of theseduplicated registers may be included in accelerator integration slice3390. Exemplary registers that may be initialized by a hypervisor areshown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by an operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 3384 is specific to a particular graphicsacceleration module 3346 and/or a particular graphics processing engine.It contains all information required by a graphics processing engine todo work or it can be a pointer to a memory location where an applicationhas set up a command queue of work to be completed.

FIGS. 34A-34B illustrate exemplary graphics processors, in accordancewith at least one embodiment. In at least one embodiment, any of theexemplary graphics processors may be fabricated using one or more IPcores. In addition to what is illustrated, other logic and circuits maybe included in at least one embodiment, including additional graphicsprocessors/cores, peripheral interface controllers, or general-purposeprocessor cores. In at least one embodiment, the exemplary graphicsprocessors are for use within an SoC.

FIG. 34A illustrates an exemplary graphics processor 3410 of an SoCintegrated circuit that may be fabricated using one or more IP cores, inaccordance with at least one embodiment. FIG. 34B illustrates anadditional exemplary graphics processor 3440 of an SoC integratedcircuit that may be fabricated using one or more IP cores, in accordancewith at least one embodiment. In at least one embodiment, graphicsprocessor 3410 of FIG. 34A is a low power graphics processor core. In atleast one embodiment, graphics processor 3440 of FIG. 34B is a higherperformance graphics processor core. In at least one embodiment, each ofgraphics processors 3410, 3440 can be variants of graphics processor 510of FIG. 5.

In at least one embodiment, graphics processor 3410 includes a vertexprocessor 3405 and one or more fragment processor(s) 3415A-3415N (e.g.,3415A, 3415B, 3415C, 3415D, through 3415N-1, and 3415N). In at least oneembodiment, graphics processor 3410 can execute different shaderprograms via separate logic, such that vertex processor 3405 isoptimized to execute operations for vertex shader programs, while one ormore fragment processor(s) 3415A-3415N execute fragment (e.g., pixel)shading operations for fragment or pixel shader programs. In at leastone embodiment, vertex processor 3405 performs a vertex processing stageof a 3D graphics pipeline and generates primitives and vertex data. Inat least one embodiment, fragment processor(s) 3415A-3415N use primitiveand vertex data generated by vertex processor 3405 to produce aframebuffer that is displayed on a display device. In at least oneembodiment, fragment processor(s) 3415A-3415N are optimized to executefragment shader programs as provided for in an OpenGL API, which may beused to perform similar operations as a pixel shader program as providedfor in a Direct 3D API.

In at least one embodiment, graphics processor 3410 additionallyincludes one or more MMU(s) 3420A-3420B, cache(s) 3425A-3425B, andcircuit interconnect(s) 3430A-3430B. In at least one embodiment, one ormore MMU(s) 3420A-3420B provide for virtual to physical address mappingfor graphics processor 3410, including for vertex processor 3405 and/orfragment processor(s) 3415A-3415N, which may reference vertex orimage/texture data stored in memory, in addition to vertex orimage/texture data stored in one or more cache(s) 3425A-3425B. In atleast one embodiment, one or more MMU(s) 3420A-3420B may be synchronizedwith other MMUs within a system, including one or more MMUs associatedwith one or more application processor(s) 505, image processors 515,and/or video processors 520 of FIG. 5, such that each processor 505-520can participate in a shared or unified virtual memory system. In atleast one embodiment, one or more circuit interconnect(s) 3430A-3430Benable graphics processor 3410 to interface with other IP cores withinan SoC, either via an internal bus of an SoC or via a direct connection.

In at least one embodiment, graphics processor 3440 includes one or moreMMU(s) 3420A-3420B, caches 3425A-3425B, and circuit interconnects3430A-3430B of graphics processor 3410 of FIG. 34A. In at least oneembodiment, graphics processor 3440 includes one or more shader core(s)3455A-3455N (e.g., 3455A, 3455B, 3455C, 3455D, 3455E, 3455F, through3455N-1, and 3455N), which provides for a unified shader corearchitecture in which a single core or type or core can execute alltypes of programmable shader code, including shader program code toimplement vertex shaders, fragment shaders, and/or compute shaders. Inat least one embodiment, a number of shader cores can vary. In at leastone embodiment, graphics processor 3440 includes an inter-core taskmanager 3445, which acts as a thread dispatcher to dispatch executionthreads to one or more shader cores 3455A-3455N and a tiling unit 3458to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, in atleast one embodiment to exploit local spatial coherence within a sceneor to optimize use of internal caches.

FIG. 35A illustrates a graphics core 3500, in accordance with at leastone embodiment. In at least one embodiment, graphics core 3500 may beincluded within graphics processor 2410 of FIG. 24. In at least oneembodiment, graphics core 3500 may be a unified shader core 3455A-3455Nas in FIG. 34B. In at least one embodiment, graphics core 3500 includesa shared instruction cache 3502, a texture unit 3518, and a cache/sharedmemory 3520 that are common to execution resources within graphics core3500. In at least one embodiment, graphics core 3500 can includemultiple slices 3501A-3501N or partition for each core, and a graphicsprocessor can include multiple instances of graphics core 3500. Slices3501A-3501N can include support logic including a local instructioncache 3504A-3504N, a thread scheduler 3506A-3506N, a thread dispatcher3508A-3508N, and a set of registers 3510A-3510N. In at least oneembodiment, slices 3501A-3501N can include a set of additional functionunits (“AFUs”) 3512A-3512N, floating-point units (“FPUs”) 3514A-3514N,integer arithmetic logic units (“ALUs”) 3516-3516N, addresscomputational units (“ACUs”) 3513A-3513N, double-precisionfloating-point units (“DPFPUs”) 3515A-3515N, and matrix processing units(“MPUs”) 3517A-3517N.

In at least one embodiment, FPUs 3514A-3514N can performsingle-precision (32-bit) and half-precision (16-bit) floating pointoperations, while DPFPUs 3515A-3515N perform double precision (64-bit)floating point operations. In at least one embodiment, ALUs 3516A-3516Ncan perform variable precision integer operations at 8-bit, 16-bit, and32-bit precision, and can be configured for mixed precision operations.In at least one embodiment, MPUs 3517A-3517N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. In at least one embodiment, MPUs3517-3517N can perform a variety of matrix operations to accelerate CUDAprograms, including enabling support for accelerated general matrix tomatrix multiplication (“GEMM”). In at least one embodiment, AFUs3512A-3512N can perform additional logic operations not supported byfloating-point or integer units, including trigonometric operations(e.g., Sine, Cosine, etc.).

FIG. 35B illustrates a general-purpose graphics processing unit(“GPGPU”) 3530, in accordance with at least one embodiment. In at leastone embodiment, GPGPU 3530 is highly-parallel and suitable fordeployment on a multi-chip module. In at least one embodiment, GPGPU3530 can be configured to enable highly-parallel compute operations tobe performed by an array of GPUs. In at least one embodiment, GPGPU 3530can be linked directly to other instances of GPGPU 3530 to create amulti-GPU cluster to improve execution time for CUDA programs. In atleast one embodiment, GPGPU 3530 includes a host interface 3532 toenable a connection with a host processor. In at least one embodiment,host interface 3532 is a PCIe interface. In at least one embodiment,host interface 3532 can be a vendor specific communications interface orcommunications fabric. In at least one embodiment, GPGPU 3530 receivescommands from a host processor and uses a global scheduler 3534 todistribute execution threads associated with those commands to a set ofcompute clusters 3536A-3536H. In at least one embodiment, computeclusters 3536A-3536H share a cache memory 3538. In at least oneembodiment, cache memory 3538 can serve as a higher-level cache forcache memories within compute clusters 3536A-3536H.

In at least one embodiment, GPGPU 3530 includes memory 3544A-3544Bcoupled with compute clusters 3536A-3536H via a set of memorycontrollers 3542A-3542B. In at least one embodiment, memory 3544A-3544Bcan include various types of memory devices including DRAM or graphicsrandom access memory, such as synchronous graphics random access memory(“SGRAM”), including graphics double data rate (“GDDR”) memory.

In at least one embodiment, compute clusters 3536A-3536H each include aset of graphics cores, such as graphics core 3500 of FIG. 35A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for computations associated with CUDA programs. In at least oneembodiment, at least a subset of floating point units in each of computeclusters 3536A-3536H can be configured to perform 16-bit or 32-bitfloating point operations, while a different subset of floating pointunits can be configured to perform 64-bit floating point operations.

In at least one embodiment, multiple instances of GPGPU 3530 can beconfigured to operate as a compute cluster. In at least one embodiment,compute clusters 3536A-3536H may implement any technically feasiblecommunication techniques for synchronization and data exchange. In atleast one embodiment, multiple instances of GPGPU 3530 communicate overhost interface 3532. In at least one embodiment, GPGPU 3530 includes anI/O hub 3539 that couples GPGPU 3530 with a GPU link 3540 that enables adirect connection to other instances of GPGPU 3530. In at least oneembodiment, GPU link 3540 is coupled to a dedicated GPU-to-GPU bridgethat enables communication and synchronization between multipleinstances of GPGPU 3530. In at least one embodiment GPU link 3540couples with a high speed interconnect to transmit and receive data toother GPGPUs 3530 or parallel processors. In at least one embodiment,multiple instances of GPGPU 3530 are located in separate data processingsystems and communicate via a network device that is accessible via hostinterface 3532. In at least one embodiment GPU link 3540 can beconfigured to enable a connection to a host processor in addition to oras an alternative to host interface 3532. In at least one embodiment,GPGPU 3530 can be configured to execute a CUDA program.

FIG. 36A illustrates a parallel processor 3600, in accordance with atleast one embodiment. In at least one embodiment, various components ofparallel processor 3600 may be implemented using one or more integratedcircuit devices, such as programmable processors, application specificintegrated circuits (“ASICs”), or FPGAs.

In at least one embodiment, parallel processor 3600 includes a parallelprocessing unit 3602. In at least one embodiment, parallel processingunit 3602 includes an I/O unit 3604 that enables communication withother devices, including other instances of parallel processing unit3602. In at least one embodiment, I/O unit 3604 may be directlyconnected to other devices. In at least one embodiment, I/O unit 3604connects with other devices via use of a hub or switch interface, suchas memory hub 605. In at least one embodiment, connections betweenmemory hub 605 and I/O unit 3604 form a communication link. In at leastone embodiment, I/O unit 3604 connects with a host interface 3606 and amemory crossbar 3616, where host interface 3606 receives commandsdirected to performing processing operations and memory crossbar 3616receives commands directed to performing memory operations.

In at least one embodiment, when host interface 3606 receives a commandbuffer via I/O unit 3604, host interface 3606 can direct work operationsto perform those commands to a front end 3608. In at least oneembodiment, front end 3608 couples with a scheduler 3610, which isconfigured to distribute commands or other work items to a processingarray 3612. In at least one embodiment, scheduler 3610 ensures thatprocessing array 3612 is properly configured and in a valid state beforetasks are distributed to processing array 3612. In at least oneembodiment, scheduler 3610 is implemented via firmware logic executingon a microcontroller. In at least one embodiment, microcontrollerimplemented scheduler 3610 is configurable to perform complex schedulingand work distribution operations at coarse and fine granularity,enabling rapid preemption and context switching of threads executing onprocessing array 3612. In at least one embodiment, host software canprove workloads for scheduling on processing array 3612 via one ofmultiple graphics processing doorbells. In at least one embodiment,workloads can then be automatically distributed across processing array3612 by scheduler 3610 logic within a microcontroller includingscheduler 3610.

In at least one embodiment, processing array 3612 can include up to “N”clusters (e.g., cluster 3614A, cluster 3614B, through cluster 3614N). Inat least one embodiment, each cluster 3614A-3614N of processing array3612 can execute a large number of concurrent threads. In at least oneembodiment, scheduler 3610 can allocate work to clusters 3614A-3614N ofprocessing array 3612 using various scheduling and/or work distributionalgorithms, which may vary depending on a workload arising for each typeof program or computation. In at least one embodiment, scheduling can behandled dynamically by scheduler 3610, or can be assisted in part bycompiler logic during compilation of program logic configured forexecution by processing array 3612. In at least one embodiment,different clusters 3614A-3614N of processing array 3612 can be allocatedfor processing different types of programs or for performing differenttypes of computations.

In at least one embodiment, processing array 3612 can be configured toperform various types of parallel processing operations. In at least oneembodiment, processing array 3612 is configured to performgeneral-purpose parallel compute operations. In at least one embodiment,processing array 3612 can include logic to execute processing tasksincluding filtering of video and/or audio data, performing modelingoperations, including physics operations, and performing datatransformations.

In at least one embodiment, processing array 3612 is configured toperform parallel graphics processing operations. In at least oneembodiment, processing array 3612 can include additional logic tosupport execution of such graphics processing operations, including, butnot limited to texture sampling logic to perform texture operations, aswell as tessellation logic and other vertex processing logic. In atleast one embodiment, processing array 3612 can be configured to executegraphics processing related shader programs such as, but not limited tovertex shaders, tessellation shaders, geometry shaders, and pixelshaders. In at least one embodiment, parallel processing unit 3602 cantransfer data from system memory via I/O unit 3604 for processing. In atleast one embodiment, during processing, transferred data can be storedto on-chip memory (e.g., a parallel processor memory 3622) duringprocessing, then written back to system memory.

In at least one embodiment, when parallel processing unit 3602 is usedto perform graphics processing, scheduler 3610 can be configured todivide a processing workload into approximately equal sized tasks, tobetter enable distribution of graphics processing operations to multipleclusters 3614A-3614N of processing array 3612. In at least oneembodiment, portions of processing array 3612 can be configured toperform different types of processing. In at least one embodiment, afirst portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. In at least one embodiment, intermediate dataproduced by one or more of clusters 3614A-3614N may be stored in buffersto allow intermediate data to be transmitted between clusters3614A-3614N for further processing.

In at least one embodiment, processing array 3612 can receive processingtasks to be executed via scheduler 3610, which receives commandsdefining processing tasks from front end 3608. In at least oneembodiment, processing tasks can include indices of data to beprocessed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howdata is to be processed (e.g., what program is to be executed). In atleast one embodiment, scheduler 3610 may be configured to fetch indicescorresponding to tasks or may receive indices from front end 3608. In atleast one embodiment, front end 3608 can be configured to ensureprocessing array 3612 is configured to a valid state before a workloadspecified by incoming command buffers (e.g., batch-buffers, pushbuffers, etc.) is initiated.

In at least one embodiment, each of one or more instances of parallelprocessing unit 3602 can couple with parallel processor memory 3622. Inat least one embodiment, parallel processor memory 3622 can be accessedvia memory crossbar 3616, which can receive memory requests fromprocessing array 3612 as well as I/O unit 3604. In at least oneembodiment, memory crossbar 3616 can access parallel processor memory3622 via a memory interface 3618. In at least one embodiment, memoryinterface 3618 can include multiple partition units (e.g., a partitionunit 3620A, partition unit 3620B, through partition unit 3620N) that caneach couple to a portion (e.g., memory unit) of parallel processormemory 3622. In at least one embodiment, a number of partition units3620A-3620N is configured to be equal to a number of memory units, suchthat a first partition unit 3620A has a corresponding first memory unit3624A, a second partition unit 3620B has a corresponding memory unit3624B, and an Nth partition unit 3620N has a corresponding Nth memoryunit 3624N. In at least one embodiment, a number of partition units3620A-3620N may not be equal to a number of memory devices.

In at least one embodiment, memory units 3624A-3624N can include varioustypes of memory devices, including DRAM or graphics random accessmemory, such as SGRAM, including GDDR memory. In at least oneembodiment, memory units 3624A-3624N may also include 3D stacked memory,including but not limited to high bandwidth memory (“HBM”). In at leastone embodiment, render targets, such as frame buffers or texture mapsmay be stored across memory units 3624A-3624N, allowing partition units3620A-3620N to write portions of each render target in parallel toefficiently use available bandwidth of parallel processor memory 3622.In at least one embodiment, a local instance of parallel processormemory 3622 may be excluded in favor of a unified memory design thatutilizes system memory in conjunction with local cache memory.

In at least one embodiment, any one of clusters 3614A-3614N ofprocessing array 3612 can process data that will be written to any ofmemory units 3624A-3624N within parallel processor memory 3622. In atleast one embodiment, memory crossbar 3616 can be configured to transferan output of each cluster 3614A-3614N to any partition unit 3620A-3620Nor to another cluster 3614A-3614N, which can perform additionalprocessing operations on an output. In at least one embodiment, eachcluster 3614A-3614N can communicate with memory interface 3618 throughmemory crossbar 3616 to read from or write to various external memorydevices. In at least one embodiment, memory crossbar 3616 has aconnection to memory interface 3618 to communicate with I/O unit 3604,as well as a connection to a local instance of parallel processor memory3622, enabling processing units within different clusters 3614A-3614N tocommunicate with system memory or other memory that is not local toparallel processing unit 3602. In at least one embodiment, memorycrossbar 3616 can use virtual channels to separate traffic streamsbetween clusters 3614A-3614N and partition units 3620A-3620N.

In at least one embodiment, multiple instances of parallel processingunit 3602 can be provided on a single add-in card, or multiple add-incards can be interconnected. In at least one embodiment, differentinstances of parallel processing unit 3602 can be configured tointeroperate even if different instances have different numbers ofprocessing cores, different amounts of local parallel processor memory,and/or other configuration differences. In at least one embodiment, someinstances of parallel processing unit 3602 can include higher precisionfloating point units relative to other instances. In at least oneembodiment, systems incorporating one or more instances of parallelprocessing unit 3602 or parallel processor 3600 can be implemented in avariety of configurations and form factors, including but not limited todesktop, laptop, or handheld personal computers, servers, workstations,game consoles, and/or embedded systems.

FIG. 36B illustrates a processing cluster 3694, in accordance with atleast one embodiment. In at least one embodiment, processing cluster3694 is included within a parallel processing unit. In at least oneembodiment, processing cluster 3694 is one of processing clusters3614A-3614N of FIG. 36. In at least one embodiment, processing cluster3694 can be configured to execute many threads in parallel, where theterm “thread” refers to an instance of a particular program executing ona particular set of input data. In at least one embodiment, singleinstruction, multiple data (“SIMD”) instruction issue techniques areused to support parallel execution of a large number of threads withoutproviding multiple independent instruction units. In at least oneembodiment, single instruction, multiple thread (“SIMT”) techniques areused to support parallel execution of a large number of generallysynchronized threads, using a common instruction unit configured toissue instructions to a set of processing engines within each processingcluster 3694.

In at least one embodiment, operation of processing cluster 3694 can becontrolled via a pipeline manager 3632 that distributes processing tasksto SIMT parallel processors. In at least one embodiment, pipelinemanager 3632 receives instructions from scheduler 3610 of FIG. 36 andmanages execution of those instructions via a graphics multiprocessor3634 and/or a texture unit 3636. In at least one embodiment, graphicsmultiprocessor 3634 is an exemplary instance of a SIMT parallelprocessor. However, in at least one embodiment, various types of SIMTparallel processors of differing architectures may be included withinprocessing cluster 3694. In at least one embodiment, one or moreinstances of graphics multiprocessor 3634 can be included withinprocessing cluster 3694. In at least one embodiment, graphicsmultiprocessor 3634 can process data and a data crossbar 3640 can beused to distribute processed data to one of multiple possibledestinations, including other shader units. In at least one embodiment,pipeline manager 3632 can facilitate distribution of processed data byspecifying destinations for processed data to be distributed via datacrossbar 3640.

In at least one embodiment, each graphics multiprocessor 3634 withinprocessing cluster 3694 can include an identical set of functionalexecution logic (e.g., arithmetic logic units, load/store units(“LSUs”), etc.). In at least one embodiment, functional execution logiccan be configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. In at least oneembodiment, functional execution logic supports a variety of operationsincluding integer and floating point arithmetic, comparison operations,Boolean operations, bit-shifting, and computation of various algebraicfunctions. In at least one embodiment, same functional-unit hardware canbe leveraged to perform different operations and any combination offunctional units may be present.

In at least one embodiment, instructions transmitted to processingcluster 3694 constitute a thread. In at least one embodiment, a set ofthreads executing across a set of parallel processing engines is athread group. In at least one embodiment, a thread group executes aprogram on different input data. In at least one embodiment, each threadwithin a thread group can be assigned to a different processing enginewithin graphics multiprocessor 3634. In at least one embodiment, athread group may include fewer threads than a number of processingengines within graphics multiprocessor 3634. In at least one embodiment,when a thread group includes fewer threads than a number of processingengines, one or more of processing engines may be idle during cycles inwhich that thread group is being processed. In at least one embodiment,a thread group may also include more threads than a number of processingengines within graphics multiprocessor 3634. In at least one embodiment,when a thread group includes more threads than a number of processingengines within graphics multiprocessor 3634, processing can be performedover consecutive clock cycles. In at least one embodiment, multiplethread groups can be executed concurrently on graphics multiprocessor3634.

In at least one embodiment, graphics multiprocessor 3634 includes aninternal cache memory to perform load and store operations. In at leastone embodiment, graphics multiprocessor 3634 can forego an internalcache and use a cache memory (e.g., L1 cache 3648) within processingcluster 3694. In at least one embodiment, each graphics multiprocessor3634 also has access to Level 2 (“L2”) caches within partition units(e.g., partition units 3620A-3620N of FIG. 36A) that are shared amongall processing clusters 3694 and may be used to transfer data betweenthreads. In at least one embodiment, graphics multiprocessor 3634 mayalso access off-chip global memory, which can include one or more oflocal parallel processor memory and/or system memory. In at least oneembodiment, any memory external to parallel processing unit 3602 may beused as global memory. In at least one embodiment, processing cluster3694 includes multiple instances of graphics multiprocessor 3634 thatcan share common instructions and data, which may be stored in L1 cache3648.

In at least one embodiment, each processing cluster 3694 may include anMMU 3645 that is configured to map virtual addresses into physicaladdresses. In at least one embodiment, one or more instances of MMU 3645may reside within memory interface 3618 of FIG. 36. In at least oneembodiment, MMU 3645 includes a set of page table entries (“PTEs”) usedto map a virtual address to a physical address of a tile and optionallya cache line index. In at least one embodiment, MMU 3645 may includeaddress translation lookaside buffers (“TLBs”) or caches that may residewithin graphics multiprocessor 3634 or L1 cache 3648 or processingcluster 3694. In at least one embodiment, a physical address isprocessed to distribute surface data access locality to allow efficientrequest interleaving among partition units. In at least one embodiment,a cache line index may be used to determine whether a request for acache line is a hit or miss.

In at least one embodiment, processing cluster 3694 may be configuredsuch that each graphics multiprocessor 3634 is coupled to a texture unit3636 for performing texture mapping operations, e.g., determiningtexture sample positions, reading texture data, and filtering texturedata. In at least one embodiment, texture data is read from an internaltexture L1 cache (not shown) or from an L1 cache within graphicsmultiprocessor 3634 and is fetched from an L2 cache, local parallelprocessor memory, or system memory, as needed. In at least oneembodiment, each graphics multiprocessor 3634 outputs a processed taskto data crossbar 3640 to provide a processed task to another processingcluster 3694 for further processing or to store a processed task in anL2 cache, a local parallel processor memory, or a system memory viamemory crossbar 3616. In at least one embodiment, a pre-rasteroperations unit (“preROP”) 3642 is configured to receive data fromgraphics multiprocessor 3634, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units3620A-3620N of FIG. 36). In at least one embodiment, PreROP 3642 canperform optimizations for color blending, organize pixel color data, andperform address translations.

FIG. 36C illustrates a graphics multiprocessor 3696, in accordance withat least one embodiment. In at least one embodiment, graphicsmultiprocessor 3696 is graphics multiprocessor 3634 of FIG. 36B. In atleast one embodiment, graphics multiprocessor 3696 couples with pipelinemanager 3632 of processing cluster 3694. In at least one embodiment,graphics multiprocessor 3696 has an execution pipeline including but notlimited to an instruction cache 3652, an instruction unit 3654, anaddress mapping unit 3656, a register file 3658, one or more GPGPU cores3662, and one or more LSUs 3666. GPGPU cores 3662 and LSUs 3666 arecoupled with cache memory 3672 and shared memory 3670 via a memory andcache interconnect 3668.

In at least one embodiment, instruction cache 3652 receives a stream ofinstructions to execute from pipeline manager 3632. In at least oneembodiment, instructions are cached in instruction cache 3652 anddispatched for execution by instruction unit 3654. In at least oneembodiment, instruction unit 3654 can dispatch instructions as threadgroups (e.g., warps), with each thread of a thread group assigned to adifferent execution unit within GPGPU core 3662. In at least oneembodiment, an instruction can access any of a local, shared, or globaladdress space by specifying an address within a unified address space.In at least one embodiment, address mapping unit 3656 can be used totranslate addresses in a unified address space into a distinct memoryaddress that can be accessed by LSUs 3666.

In at least one embodiment, register file 3658 provides a set ofregisters for functional units of graphics multiprocessor 3696. In atleast one embodiment, register file 3658 provides temporary storage foroperands connected to data paths of functional units (e.g., GPGPU cores3662, LSUs 3666) of graphics multiprocessor 3696. In at least oneembodiment, register file 3658 is divided between each of functionalunits such that each functional unit is allocated a dedicated portion ofregister file 3658. In at least one embodiment, register file 3658 isdivided between different thread groups being executed by graphicsmultiprocessor 3696.

In at least one embodiment, GPGPU cores 3662 can each include FPUsand/or integer ALUs that are used to execute instructions of graphicsmultiprocessor 3696. GPGPU cores 3662 can be similar in architecture orcan differ in architecture. In at least one embodiment, a first portionof GPGPU cores 3662 include a single precision FPU and an integer ALUwhile a second portion of GPGPU cores 3662 include a double precisionFPU. In at least one embodiment, FPUs can implement IEEE 754-2008standard for floating point arithmetic or enable variable precisionfloating point arithmetic. In at least one embodiment, graphicsmultiprocessor 3696 can additionally include one or more fixed functionor special function units to perform specific functions such as copyrectangle or pixel blending operations. In at least one embodiment oneor more of GPGPU cores 3662 can also include fixed or special functionlogic.

In at least one embodiment, GPGPU cores 3662 include SIMD logic capableof performing a single instruction on multiple sets of data. In at leastone embodiment GPGPU cores 3662 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. In at least one embodiment, SIMD instructions for GPGPUcores 3662 can be generated at compile time by a shader compiler orautomatically generated when executing programs written and compiled forsingle program multiple data (“SPMD”) or SIMT architectures. In at leastone embodiment, multiple threads of a program configured for an SIMTexecution model can executed via a single SIMD instruction. In at leastone embodiment, eight SIMT threads that perform the same or similaroperations can be executed in parallel via a single SIMD8 logic unit.

In at least one embodiment, memory and cache interconnect 3668 is aninterconnect network that connects each functional unit of graphicsmultiprocessor 3696 to register file 3658 and to shared memory 3670. Inat least one embodiment, memory and cache interconnect 3668 is acrossbar interconnect that allows LSU 3666 to implement load and storeoperations between shared memory 3670 and register file 3658. In atleast one embodiment, register file 3658 can operate at a same frequencyas GPGPU cores 3662, thus data transfer between GPGPU cores 3662 andregister file 3658 is very low latency. In at least one embodiment,shared memory 3670 can be used to enable communication between threadsthat execute on functional units within graphics multiprocessor 3696. Inat least one embodiment, cache memory 3672 can be used as a data cachein at least one embodiment, to cache texture data communicated betweenfunctional units and texture unit 3636. In at least one embodiment,shared memory 3670 can also be used as a program managed cached. In atleast one embodiment, threads executing on GPGPU cores 3662 canprogrammatically store data within shared memory in addition toautomatically cached data that is stored within cache memory 3672.

In at least one embodiment, a parallel processor or GPGPU as describedherein is communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. In atleast one embodiment, a GPU may be communicatively coupled to hostprocessor/cores over a bus or other interconnect (e.g., a high speedinterconnect such as PCIe or NVLink). In at least one embodiment, a GPUmay be integrated on a same package or chip as cores and communicativelycoupled to cores over a processor bus/interconnect that is internal to apackage or a chip. In at least one embodiment, regardless of a manner inwhich a GPU is connected, processor cores may allocate work to a GPU ina form of sequences of commands/instructions contained in a WD. In atleast one embodiment, a GPU then uses dedicated circuitry/logic forefficiently processing these commands/instructions.

General Computing

The following FIG.s set forth, without limitation, exemplary softwareconstructs within general computing that can be used to implement atleast one embodiment.

FIG. 37 illustrates a software stack of a programming platform, inaccordance with at least one embodiment. In at least one embodiment, aprogramming platform is a platform for leveraging hardware on acomputing system to accelerate computational tasks. A programmingplatform may be accessible to software developers through libraries,compiler directives, and/or extensions to programming languages, in atleast one embodiment. In at least one embodiment, a programming platformmay be, but is not limited to, CUDA, Radeon Open Compute Platform(“ROCm”), OpenCL (OpenCL™ is developed by Khronos group), SYCL, or IntelOne API.

In at least one embodiment, a software stack 3700 of a programmingplatform provides an execution environment for an application 3701. Inat least one embodiment, application 3701 may include any computersoftware capable of being launched on software stack 3700. In at leastone embodiment, application 3701 may include, but is not limited to, anartificial intelligence (“AI”)/machine learning (“ML”) application, ahigh performance computing (“HPC”) application, a virtual desktopinfrastructure (“VDI”), or a datacenter workload.

In at least one embodiment, application 3701 and software stack 3700 runon hardware 3707. Hardware 3707 may include one or more GPUs, CPUs,FPGAs, AI engines, and/or other types of compute devices that support aprogramming platform, in at least one embodiment. In at least oneembodiment, such as with CUDA, software stack 3700 may be vendorspecific and compatible with only devices from particular vendor(s). Inat least one embodiment, such as in with OpenCL, software stack 3700 maybe used with devices from different vendors. In at least one embodiment,hardware 3707 includes a host connected to one more devices that can beaccessed to perform computational tasks via application programminginterface (“API”) calls. A device within hardware 3707 may include, butis not limited to, a GPU, FPGA, AI engine, or other compute device (butmay also include a CPU) and its memory, as opposed to a host withinhardware 3707 that may include, but is not limited to, a CPU (but mayalso include a compute device) and its memory, in at least oneembodiment.

In at least one embodiment, software stack 3700 of a programmingplatform includes, without limitation, a number of libraries 3703, aruntime 3705, and a device kernel driver 3706. Each of libraries 3703may include data and programming code that can be used by computerprograms and leveraged during software development, in at least oneembodiment. In at least one embodiment, libraries 3703 may include, butare not limited to, pre-written code and subroutines, classes, values,type specifications, configuration data, documentation, help data,and/or message templates. In at least one embodiment, libraries 3703include functions that are optimized for execution on one or more typesof devices. In at least one embodiment, libraries 3703 may include, butare not limited to, functions for performing mathematical, deeplearning, and/or other types of operations on devices. In at least oneembodiment, libraries 3803 are associated with corresponding APIs 3802,which may include one or more APIs, that expose functions implemented inlibraries 3803.

In at least one embodiment, application 3701 is written as source codethat is compiled into executable code, as discussed in greater detailbelow in conjunction with FIG. 42. Executable code of application 3701may run, at least in part, on an execution environment provided bysoftware stack 3700, in at least one embodiment. In at least oneembodiment, during execution of application 3701, code may be reachedthat needs to run on a device, as opposed to a host. In such a case,runtime 3705 may be called to load and launch requisite code on adevice, in at least one embodiment. In at least one embodiment, runtime3705 may include any technically feasible runtime system that is able tosupport execution of application S01.

In at least one embodiment, runtime 3705 is implemented as one or moreruntime libraries associated with corresponding APIs, which are shown asAPI(s) 3704. One or more of such runtime libraries may include, withoutlimitation, functions for memory management, execution control, devicemanagement, error handling, and/or synchronization, among other things,in at least one embodiment. In at least one embodiment, memorymanagement functions may include, but are not limited to, functions toallocate, deallocate, and copy device memory, as well as transfer databetween host memory and device memory. In at least one embodiment,execution control functions may include, but are not limited to,functions to launch a function (sometimes referred to as a “kernel” whena function is a global function callable from a host) on a device andset attribute values in a buffer maintained by a runtime library for agiven function to be executed on a device.

Runtime libraries and corresponding API(s) 3704 may be implemented inany technically feasible manner, in at least one embodiment. In at leastone embodiment, one (or any number of) API may expose a low-level set offunctions for fine-grained control of a device, while another (or anynumber of) API may expose a higher-level set of such functions. In atleast one embodiment, a high-level runtime API may be built on top of alow-level API. In at least one embodiment, one or more of runtime APIsmay be language-specific APIs that are layered on top of alanguage-independent runtime API.

In at least one embodiment, device kernel driver 3706 is configured tofacilitate communication with an underlying device. In at least oneembodiment, device kernel driver 3706 may provide low-levelfunctionalities upon which APIs, such as API(s) 3704, and/or othersoftware relies. In at least one embodiment, device kernel driver 3706may be configured to compile intermediate representation (“IR”) codeinto binary code at runtime. For CUDA, device kernel driver 3706 maycompile Parallel Thread Execution (“PTX”) IR code that is not hardwarespecific into binary code for a specific target device at runtime (withcaching of compiled binary code), which is also sometimes referred to as“finalizing” code, in at least one embodiment. Doing so may permitfinalized code to run on a target device, which may not have existedwhen source code was originally compiled into PTX code, in at least oneembodiment. Alternatively, in at least one embodiment, device sourcecode may be compiled into binary code offline, without requiring devicekernel driver 3706 to compile IR code at runtime.

FIG. 38 illustrates a CUDA implementation of software stack 3700 of FIG.37, in accordance with at least one embodiment. In at least oneembodiment, a CUDA software stack 3800, on which an application 3801 maybe launched, includes CUDA libraries 3803, a CUDA runtime 3805, a CUDAdriver 3807, and a device kernel driver 3808. In at least oneembodiment, CUDA software stack 3800 executes on hardware 3809, whichmay include a GPU that supports CUDA and is developed by NVIDIACorporation of Santa Clara, Calif.

In at least one embodiment, application 3801, CUDA runtime 3805, anddevice kernel driver 3808 may perform similar functionalities asapplication 3701, runtime 3705, and device kernel driver 3706,respectively, which are described above in conjunction with FIG. 37. Inat least one embodiment, CUDA driver 3807 includes a library(libcuda.so) that implements a CUDA driver API 3806. Similar to a CUDAruntime API 3804 implemented by a CUDA runtime library (cudart), CUDAdriver API 3806 may, without limitation, expose functions for memorymanagement, execution control, device management, error handling,synchronization, and/or graphics interoperability, among other things,in at least one embodiment. In at least one embodiment, CUDA driver API3806 differs from CUDA runtime API 3804 in that CUDA runtime API 3804simplifies device code management by providing implicit initialization,context (analogous to a process) management, and module (analogous todynamically loaded libraries) management. In contrast to high-level CUDAruntime API 3804, CUDA driver API 3806 is a low-level API providing morefine-grained control of a device, particularly with respect to contextsand module loading, in at least one embodiment. In at least oneembodiment, CUDA driver API 3806 may expose functions for contextmanagement that are not exposed by CUDA runtime API 3804. In at leastone embodiment, CUDA driver API 3806 is also language-independent andsupports, e.g., OpenCL in addition to CUDA runtime API 3804. Further, inat least one embodiment, development libraries, including CUDA runtime3805, may be considered as separate from driver components, includinguser-mode CUDA driver 3807 and kernel-mode device driver 3808 (alsosometimes referred to as a “display” driver).

In at least one embodiment, CUDA libraries 3803 may include, but are notlimited to, mathematical libraries, deep learning libraries, parallelalgorithm libraries, and/or signal/image/video processing libraries,which parallel computing applications such as application 3801 mayutilize. In at least one embodiment, CUDA libraries 3803 may includemathematical libraries such as a cuBLAS library that is animplementation of Basic Linear Algebra Subprograms (“BLAS”) forperforming linear algebra operations, a cuFFT library for computing fastFourier transforms (“FFTs”), and a cuRAND library for generating randomnumbers, among others. In at least one embodiment, CUDA libraries 3803may include deep learning libraries such as a cuDNN library ofprimitives for deep neural networks and a TensorRT platform forhigh-performance deep learning inference, among others.

FIG. 39 illustrates a ROCm implementation of software stack 3700 of FIG.37, in accordance with at least one embodiment. In at least oneembodiment, a ROCm software stack 3900, on which an application 3901 maybe launched, includes a language runtime 3903, a system runtime 3905, athunk 3907, a ROCm kernel driver 3908, and a device kernel driver 3909.In at least one embodiment, ROCm software stack 3900 executes onhardware 3910, which may include a GPU that supports ROCm and isdeveloped by AMD Corporation of Santa Clara, Calif.

In at least one embodiment, application 3901 may perform similarfunctionalities as application 3701 discussed above in conjunction withFIG. 37. In addition, language runtime 3903 and system runtime 3905 mayperform similar functionalities as runtime 3705 discussed above inconjunction with FIG. 37, in at least one embodiment. In at least oneembodiment, language runtime 3903 and system runtime 3905 differ in thatsystem runtime 3905 is a language-independent runtime that implements aROCr system runtime API 3904 and makes use of a Heterogeneous SystemArchitecture (“HAS”) Runtime API. HAS runtime API is a thin, user-modeAPI that exposes interfaces to access and interact with an AMD GPU,including functions for memory management, execution control viaarchitected dispatch of kernels, error handling, system and agentinformation, and runtime initialization and shutdown, among otherthings, in at least one embodiment. In contrast to system runtime 3905,language runtime 3903 is an implementation of a language-specificruntime API 3902 layered on top of ROCr system runtime API 3904, in atleast one embodiment. In at least one embodiment, language runtime APImay include, but is not limited to, a Heterogeneous compute Interfacefor Portability (“HIP”) language runtime API, a Heterogeneous ComputeCompiler (“HCC”) language runtime API, or an OpenCL API, among others.HIP language in particular is an extension of C++ programming languagewith functionally similar versions of CUDA mechanisms, and, in at leastone embodiment, a HIP language runtime API includes functions that aresimilar to those of CUDA runtime API 3804 discussed above in conjunctionwith FIG. 38, such as functions for memory management, executioncontrol, device management, error handling, and synchronization, amongother things.

In at least one embodiment, thunk (ROCt) 3907 is an interface that canbe used to interact with underlying ROCm driver 3908. In at least oneembodiment, ROCm driver 3908 is a ROCk driver, which is a combination ofan AMDGPU driver and a HAS kernel driver (amdkfd). In at least oneembodiment, AMDGPU driver is a device kernel driver for GPUs developedby AMD that performs similar functionalities as device kernel driver3706 discussed above in conjunction with FIG. 37. In at least oneembodiment, HAS kernel driver is a driver permitting different types ofprocessors to share system resources more effectively via hardwarefeatures.

In at least one embodiment, various libraries (not shown) may beincluded in ROCm software stack 3900 above language runtime 3903 andprovide functionality similarity to CUDA libraries 3803, discussed abovein conjunction with FIG. 38. In at least one embodiment, variouslibraries may include, but are not limited to, mathematical, deeplearning, and/or other libraries such as a hipBLAS library thatimplements functions similar to those of CUDA cuBLAS, a rocFFT libraryfor computing FFTs that is similar to CUDA cuFFT, among others.

FIG. 40 illustrates an OpenCL implementation of software stack 3700 ofFIG. 37, in accordance with at least one embodiment. In at least oneembodiment, an OpenCL software stack 4000, on which an application 4001may be launched, includes an OpenCL framework 4005, an OpenCL runtime4006, and a driver 4007. In at least one embodiment, OpenCL softwarestack 4000 executes on hardware 3809 that is not vendor-specific. AsOpenCL is supported by devices developed by different vendors, specificOpenCL drivers may be required to interoperate with hardware from suchvendors, in at least one embodiment.

In at least one embodiment, application 4001, OpenCL runtime 4006,device kernel driver 4007, and hardware 4008 may perform similarfunctionalities as application 3701, runtime 3705, device kernel driver3706, and hardware 3707, respectively, that are discussed above inconjunction with FIG. 37. In at least one embodiment, application 4001further includes an OpenCL kernel 4002 with code that is to be executedon a device.

In at least one embodiment, OpenCL defines a “platform” that allows ahost to control devices connected to a host. In at least one embodiment,an OpenCL framework provides a platform layer API and a runtime API,shown as platform API 4003 and runtime API 4005. In at least oneembodiment, runtime API 4005 uses contexts to manage execution ofkernels on devices. In at least one embodiment, each identified devicemay be associated with a respective context, which runtime API 4005 mayuse to manage command queues, program objects, and kernel objects, sharememory objects, among other things, for that device. In at least oneembodiment, platform API 4003 exposes functions that permit devicecontexts to be used to select and initialize devices, submit work todevices via command queues, and enable data transfer to and fromdevices, among other things. In addition, OpenCL framework providesvarious built-in functions (not shown), including math functions,relational functions, and image processing functions, among others, inat least one embodiment.

In at least one embodiment, a compiler 4004 is also included in OpenCLframework 4005. Source code may be compiled offline prior to executingan application or online during execution of an application, in at leastone embodiment. In contrast to CUDA and ROCm, OpenCL applications in atleast one embodiment may be compiled online by compiler 4004, which isincluded to be representative of any number of compilers that may beused to compile source code and/or IR code, such as Standard PortableIntermediate Representation (“SPIR-V”) code, into binary code.Alternatively, in at least one embodiment, OpenCL applications may becompiled offline, prior to execution of such applications.

FIG. 41 illustrates software that is supported by a programmingplatform, in accordance with at least one embodiment. In at least oneembodiment, a programming platform 4104 is configured to support variousprogramming models 4103, middlewares and/or libraries 4102, andframeworks 4101 that an application 4100 may rely upon. In at least oneembodiment, application 4100 may be an AI/ML application implementedusing, in at least one embodiment, a deep learning framework such asMXNet, PyTorch, or TensorFlow, which may rely on libraries such ascuDNN, NVIDIA Collective Communications Library (“NCCL”), and/or NVIDADeveloper Data Loading Library (“DALI”) CUDA libraries to provideaccelerated computing on underlying hardware.

In at least one embodiment, programming platform 4104 may be one of aCUDA, ROCm, or OpenCL platform described above in conjunction with FIG.33, FIG. 34, and FIG. 40, respectively. In at least one embodiment,programming platform 4104 supports multiple programming models 4103,which are abstractions of an underlying computing system permittingexpressions of algorithms and data structures. Programming models 4103may expose features of underlying hardware in order to improveperformance, in at least one embodiment. In at least one embodiment,programming models 4103 may include, but are not limited to, CUDA, HIP,OpenCL, C++ Accelerated Massive Parallelism (“C++ AMP”), OpenMulti-Processing (“OpenMP”), Open Accelerators (“OpenACC”), and/orVulcan Compute.

In at least one embodiment, libraries and/or middlewares 4102 provideimplementations of abstractions of programming models 4104. In at leastone embodiment, such libraries include data and programming code thatmay be used by computer programs and leveraged during softwaredevelopment. In at least one embodiment, such middlewares includesoftware that provides services to applications beyond those availablefrom programming platform 4104. In at least one embodiment, librariesand/or middlewares 4102 may include, but are not limited to, cuBLAS,cuFFT, cuRAND, and other CUDA libraries, or rocBLAS, rocFFT, rocRAND,and other ROCm libraries. In addition, in at least one embodiment,libraries and/or middlewares 4102 may include NCCL and ROCmCommunication Collectives Library (“RCCL”) libraries providingcommunication routines for GPUs, a MIOpen library for deep learningacceleration, and/or an Eigen library for linear algebra, matrix andvector operations, geometrical transformations, numerical solvers, andrelated algorithms.

In at least one embodiment, application frameworks 4101 depend onlibraries and/or middlewares 4102. In at least one embodiment, each ofapplication frameworks 4101 is a software framework used to implement astandard structure of application software. An AWL application may beimplemented using a framework such as Caffe, Caffe2, TensorFlow, Keras,PyTorch, or MxNet deep learning frameworks, in at least one embodiment.

FIG. 42 illustrates compiling code to execute on one of programmingplatforms of FIGS. 37-40, in accordance with at least one embodiment. Inat least one embodiment, a compiler 4201 receives source code 4200 thatincludes both host code as well as device code. In at least oneembodiment, complier 4201 is configured to convert source code 4200 intohost executable code 4202 for execution on a host and device executablecode 4203 for execution on a device. In at least one embodiment, sourcecode 4200 may either be compiled offline prior to execution of anapplication, or online during execution of an application.

In at least one embodiment, source code 4200 may include code in anyprogramming language supported by compiler 4201, such as C++, C,Fortran, etc. In at least one embodiment, source code 4200 may beincluded in a single-source file having a mixture of host code anddevice code, with locations of device code being indicated therein. Inat least one embodiment, a single-source file may be a .cu file thatincludes CUDA code or a .hip.cpp file that includes HIP code.Alternatively, in at least one embodiment, source code 4200 may includemultiple source code files, rather than a single-source file, into whichhost code and device code are separated.

In at least one embodiment, compiler 4201 is configured to compilesource code 4200 into host executable code 4202 for execution on a hostand device executable code 4203 for execution on a device. In at leastone embodiment, compiler 4201 performs operations including parsingsource code 4200 into an abstract system tree (AST), performingoptimizations, and generating executable code. In at least oneembodiment in which source code 4200 includes a single-source file,compiler 4201 may separate device code from host code in such asingle-source file, compile device code and host code into deviceexecutable code 4203 and host executable code 4202, respectively, andlink device executable code 4203 and host executable code 4202 togetherin a single file, as discussed in greater detail below with respect toFIG. 26.

In at least one embodiment, host executable code 4202 and deviceexecutable code 4203 may be in any suitable format, such as binary codeand/or IR code. In a case of CUDA, host executable code 4202 may includenative object code and device executable code 4203 may include code inPTX intermediate representation, in at least one embodiment. In a caseof ROCm, both host executable code 4202 and device executable code 4203may include target binary code, in at least one embodiment.

Other variations are within spirit of present disclosure. Thus, whiledisclosed techniques are susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in drawings and have been described above in detail. It should beunderstood, however, that there is no intention to limit disclosure tospecific form or forms disclosed, but on contrary, intention is to coverall modifications, alternative constructions, and equivalents fallingwithin spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context ofdescribing disclosed embodiments (especially in context of followingclaims) are to be construed to cover both singular and plural, unlessotherwise indicated herein or clearly contradicted by context, and notas a definition of a term. Terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (meaning“including, but not limited to,”) unless otherwise noted. term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to, orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinrange, unless otherwise indicated herein and each separate value isincorporated into specification as if it were individually recitedherein. In at least one embodiment, use of term “set” (e.g., “a set ofitems”) or “subset” unless otherwise noted or contradicted by context,is to be construed as a nonempty collection comprising one or moremembers. Further, unless otherwise noted or contradicted by context,term “subset” of a corresponding set does not necessarily denote aproper subset of corresponding set, but subset and corresponding set maybe equal.

Conjunctive language, such as phrases of form “at least one of A, B, andC,” or “at least one of A, B and C,” unless specifically statedotherwise or otherwise clearly contradicted by context, is otherwiseunderstood with context as used in general to present that an item,term, etc., may be either A or B or C, or any nonempty subset of set ofA and B and C. In at least one embodiment of a set having three members,conjunctive phrases “at least one of A, B, and C” and “at least one ofA, B and C” refer to any of following sets: {A}, {B}, {C}, {A, B}, {A,C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generallyintended to imply that certain embodiments require at least one of A, atleast one of B and at least one of C each to be present. In addition,unless otherwise noted or contradicted by context, term “plurality”indicates a state of being plural (e.g., “a plurality of items”indicates multiple items). In at least one embodiment, a number of itemsin a plurality is at least two, but can be more when so indicated eitherexplicitly or by context. Further, unless stated otherwise or otherwiseclear from context, phrase “based on” means “based at least in part on”and not “based solely on.”

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. In at least one embodiment, a process such asthose processes described herein (or variations and/or combinationsthereof) is performed under control of one or more computer systemsconfigured with executable instructions and is implemented as code(e.g., executable instructions, one or more computer programs or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. In at least one embodiment, code isstored on a computer-readable storage medium. In at least oneembodiment, in form of a computer program comprising a plurality ofinstructions executable by one or more processors. In at least oneembodiment, a computer-readable storage medium is a non-transitorycomputer-readable storage medium that excludes transitory signals (e.g.,a propagating transient electric or electromagnetic transmission) butincludes non-transitory data storage circuitry (e.g., buffers, cache,and queues) within transceivers of transitory signals. In at least oneembodiment, code (e.g., executable code or source code) is stored on aset of one or more non-transitory computer-readable storage media havingstored thereon executable instructions (or other memory to storeexecutable instructions) that, when executed (i.e., as a result of beingexecuted) by one or more processors of a computer system, cause computersystem to perform operations described herein. A set of non-transitorycomputer-readable storage media, in at least one embodiment, comprisesmultiple non-transitory computer-readable storage media and one or moreof individual non-transitory storage media of multiple non-transitorycomputer-readable storage media lack all of code while multiplenon-transitory computer-readable storage media collectively store all ofcode. In at least one embodiment, executable instructions are executedsuch that different instructions are executed by different processors—inat least one embodiment, a non-transitory computer-readable storagemedium store instructions and a main central processing unit (“CPU”)executes some of instructions while a graphics processing unit (“GPU”)executes other instructions. In at least one embodiment, differentcomponents of a computer system have separate processors and differentprocessors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configuredto implement one or more services that singly or collectively performoperations of processes described herein and such computer systems areconfigured with applicable hardware and/or software that enableperformance of operations. Further, a computer system that implements atleast one embodiment of present disclosure is a single device and, inanother embodiment, is a distributed computer system comprising multipledevices that operate differently such that distributed computer systemperforms operations described herein and such that a single device doesnot perform all operations.

Use of any and all of the at least one embodiments, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate embodiments of disclosure and does not pose a limitation onscope of disclosure unless otherwise claimed. No language inspecification should be construed as indicating any non-claimed elementas essential to practice of disclosure.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In description and claims, terms “coupled” and “connected,” along withtheir derivatives, may be used. It should be understood that these termsmay be not intended as synonyms for each other. Rather, in ones of atleast one embodiments, “connected” or “coupled” may be used to indicatethat two or more elements are in direct or indirect physical orelectrical contact with each other. “Coupled” may also mean that two ormore elements are not in direct contact with each other, but yet stillco-operate or interact with each other.

Unless specifically stated otherwise, it may be appreciated thatthroughout specification terms such as “processing,” “computing,”“calculating,” “determining,” or like, refer to action and/or processesof a computer or computing system, or similar electronic computingdevice, that manipulate and/or transform data represented as physical,such as electronic, quantities within computing system's registersand/or memories into other data similarly represented as physicalquantities within computing system's memories, registers or other suchinformation storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryand transform that electronic data into other electronic data that maybe stored in registers and/or memory. As non-limiting ones of the atleast one embodiments, “processor” may be a CPU or a GPU. A “computingplatform” may comprise one or more processors. As used herein,“software” processes may include, in at least one embodiment, softwareand/or hardware entities that perform work over time, such as tasks,threads, and intelligent agents. Also, each process may refer tomultiple processes, for carrying out instructions in sequence or inparallel, continuously or intermittently. Terms “system” and “method”are used herein interchangeably insofar as system may embody one or moremethods and methods may be considered a system.

In present document, references may be made to obtaining, acquiring,receiving, or inputting analog or digital data into a subsystem,computer system, or computer-implemented machine. In at least oneembodiment, process of obtaining, acquiring, receiving, or inputtinganalog and digital data can be accomplished in a variety of ways such asby receiving data as a parameter of a function call or a call to anapplication programming interface. In some implementations, process ofobtaining, acquiring, receiving, or inputting analog or digital data canbe accomplished by transferring data via a serial or parallel interface.In another implementation, process of obtaining, acquiring, receiving,or inputting analog or digital data can be accomplished by transferringdata via a computer network from providing entity to acquiring entity.References may also be made to providing, outputting, transmitting,sending, or presenting analog or digital data. In various ones of the atleast one embodiments, process of providing, outputting, transmitting,sending, or presenting analog or digital data can be accomplished bytransferring data as an input or output parameter of a function call, aparameter of an application programming interface or interprocesscommunication mechanism.

Although discussion above sets forth ones of the at least oneembodiments having implementations of described techniques, otherarchitectures may be used to implement described functionality, and areintended to be within scope of this disclosure. Furthermore, althoughspecific distributions of responsibilities are defined above forpurposes of discussion, various functions and responsibilities might bedistributed and divided in different ways, depending on circumstances.

Furthermore, although subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that subject matter claimed in appended claims is notnecessarily limited to specific features or acts described. Rather,specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A processor, comprising: one or more circuits touse one or more neural networks to determine one or more temperaturecontrol settings associated with one or more servers.
 2. The processorof claim 1, wherein the one or more neural networks are further topredict one or more future temperature values at one or more locationsat one or more future points in time, and wherein the one or moretemperature control settings are determined based at least in part uponthe one or more future temperature values.
 3. The processor of claim 1,wherein the one or more neural networks are stored on one or morecomputing boards in one or more temperature control devices associatedwith the one or more temperature control settings.
 4. The processor ofclaim 1, wherein the one or more temperature control settings aredetermined for at least one of: individual servers of the one or moreservers, individual racks including the one or more servers, or a datacenter including the one or more servers, and wherein the one or moreneural networks can further be used to determine one or moreenvironmental control settings relating to at least one of power,humidity, fluid flow, or power.
 5. The processor of claim 1, wherein theone or more circuits are further to utilize the one or more neuralnetworks over time to determine whether to update the one or moretemperature control settings based upon updated predictions generated bythe one or more neural networks.
 6. The processor of claim 1, whereinthe one or more neural networks accept environmental data from at leastone of a temperature sensor, a pressure sensor, a flow sensor, a powersensor, a humidity sensor, or a load determination component associatedwith the one or more servers.
 7. A system comprising: one or moreprocessors to use one or more neural networks to determine one or moretemperature control settings associated with one or more servers.
 8. Thesystem of claim 7, wherein the one or more neural networks are furtherto predict one or more future temperature values at one or morelocations at one or more future points in time, and wherein the one ormore temperature control settings are determined based at least in partupon the one or more future temperature values.
 9. The system of claim7, wherein the one or more neural networks are stored on one or morecomputing boards in one or more temperature control devices associatedwith the one or more temperature control settings.
 10. The system ofclaim 7, wherein the one or more temperature control settings aredetermined for at least one of: individual servers of the one or moreservers, individual racks including the one or more servers, or a datacenter including the one or more servers, and wherein the one or moreneural networks can further be used to determine one or moreenvironmental control settings relating to at least one of power,humidity, fluid flow, or power.
 11. The system of claim 7, wherein theone or more processors are further to utilize the one or more neuralnetworks over time to determine whether to update the one or moretemperature control settings based upon updated predictions generated bythe one or more neural networks.
 12. The system of claim 7, wherein theone or more neural networks accept environmental data from at least oneof a temperature sensor, a pressure sensor, a flow sensor, a powersensor, a humidity sensor, or a load determination component associatedwith the one or more servers.
 13. A method comprising: using one or moreneural networks to determine one or more temperature control settingsassociated with one or more servers.
 14. The method of claim 13, whereinthe one or more neural networks are further to predict one or morefuture temperature values at one or more locations at one or more futurepoints in time, and wherein the one or more temperature control settingsare determined based at least in part upon the one or more futuretemperature values.
 15. The method of claim 13, wherein the one or moreneural networks are stored on one or more computing boards in one ormore temperature control devices associated with the one or moretemperature control settings.
 16. The method of claim 13, wherein theone or more temperature control settings are determined for at least oneof: individual servers of the one or more servers, individual racksincluding the one or more servers, or a data center including the one ormore servers, and wherein the one or more neural networks can further beused to determine one or more environmental control settings relating toat least one of power, humidity, fluid flow, or power.
 17. The method ofclaim 13, further comprising: utilizing the one or more neural networksover time to determine whether to update the one or more temperaturecontrol settings based upon updated predictions generated by the one ormore neural networks.
 18. The method of claim 13, wherein the one ormore neural networks accept environmental data from at least one of atemperature sensor, a pressure sensor, a flow sensor, a power sensor, ahumidity sensor, or a load determination component associated with theone or more servers.
 19. A machine-readable medium having stored thereona set of instructions, which if performed by one or more processors,cause the one or more processors to at least: use one or more neuralnetworks to determine one or more temperature control settingsassociated with one or more servers.
 20. The machine-readable medium ofclaim 19, wherein the one or more neural networks are further to predictone or more future temperature values at one or more locations at one ormore future points in time, and wherein the one or more temperaturecontrol settings are determined based at least in part upon the one ormore future temperature values.
 21. The machine-readable medium of claim19, wherein the one or more neural networks are stored on one or morecomputing boards in one or more temperature control devices associatedwith the one or more temperature control settings.
 22. Themachine-readable medium of claim 19, wherein the one or more temperaturecontrol settings are determined for at least one of: individual serversof the one or more servers, individual racks including the one or moreservers, or a data center including the one or more servers, and whereinthe one or more neural networks can further be used to determine one ormore environmental control settings relating to at least one of power,humidity, fluid flow, or power.
 23. The machine-readable medium of claim19, wherein the instructions if performed further cause the one or moreprocessors to: utilize the one or more neural networks over time todetermine whether to update the one or more temperature control settingsbased upon updated predictions generated by the one or more neuralnetworks.
 24. The machine-readable medium of claim 19, wherein the oneor more neural networks accept environmental data from at least one of atemperature sensor, a pressure sensor, a flow sensor, a power sensor, ahumidity sensor, or a load determination component associated with theone or more servers.
 25. A data center cooling system, comprising: oneor more cooling systems associated with one or more servers; one or moreprocessors to use one or more neural networks to determine one or moretemperature control settings for the one or more cooling systems; andmemory for storing network parameters for the one or more neuralnetworks.
 26. The data center cooling system of claim 25, wherein theone or more neural networks are further to predict one or more futuretemperature values at one or more locations at one or more future pointsin time, and wherein the one or more temperature control settings aredetermined based at least in part upon the one or more futuretemperature values.
 27. The data center cooling system of claim 25,wherein the one or more neural networks are stored on one or morecomputing boards in one or more temperature control devices associatedwith the one or more temperature control settings.
 28. The data centercooling system of claim 25, wherein the one or more temperature controlsettings are determined for at least one of: individual servers of theone or more servers, individual racks including the one or more servers,or a data center including the one or more servers, and wherein the oneor more neural networks can further be used to determine one or moreenvironmental control settings relating to at least one of power,humidity, fluid flow, or power.
 29. The data center cooling system ofclaim 25, wherein the one or more circuits are further to utilize theone or more neural networks over time to determine whether to update theone or more temperature control settings based upon updated predictionsgenerated by the one or more neural networks.
 30. The data centercooling system of claim 25, wherein the one or more neural networksaccept environmental data from at least one of a temperature sensor, apressure sensor, a flow sensor, a power sensor, a humidity sensor, or aload determination component associated with the one or more servers.